Articles | Volume 24, issue 12
https://doi.org/10.5194/nhess-24-4631-2024
© 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-24-4631-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Between global risk reduction goals, scientific–technical capabilities and local realities: a modular approach for user-centric multi-risk assessment
Elisabeth Schoepfer
CORRESPONDING AUTHOR
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Wessling, 82234, Germany
Jörn Lauterjung
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, 14473, Germany
Torsten Riedlinger
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Wessling, 82234, Germany
Harald Spahn
independent consultant: Apen, 26689, Germany
Juan Camilo Gómez Zapata
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, 14473, Germany
Institute for Geosciences, University of Potsdam, Potsdam, 14476, Germany
Christian D. León
DIALOGIK gGmbH, Stuttgart, 70176, Germany
Hugo Rosero-Velásquez
Engineering Risk Analysis Group, Technical University of Munich, Munich, 80333, Germany
Sven Harig
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, 27570, Germany
Michael Langbein
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Wessling, 82234, Germany
Nils Brinckmann
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, 14473, Germany
Günter Strunz
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Wessling, 82234, Germany
Christian Geiß
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Wessling, 82234, Germany
Department of Geography, University of Bonn, Bonn, 53115, Germany
Hannes Taubenböck
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Wessling, 82234, Germany
Institute of Geography and Geology, University of Würzburg, Würzburg, 97074, Germany
Related authors
Elisabeth Schoepfer, Rodrigo Cienfuegos, Jörn Lauterjung, Torsten Riedlinger, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 25, 1163–1167, https://doi.org/10.5194/nhess-25-1163-2025, https://doi.org/10.5194/nhess-25-1163-2025, 2025
Christian Geiß, Jana Maier, Emily So, Elisabeth Schoepfer, Sven Harig, Juan Camilo Gómez Zapata, and Yue Zhu
Nat. Hazards Earth Syst. Sci., 24, 1051–1064, https://doi.org/10.5194/nhess-24-1051-2024, https://doi.org/10.5194/nhess-24-1051-2024, 2024
Short summary
Short summary
We establish a model of future geospatial population distributions to quantify the number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru, for the year 2035. Areas of high earthquake intensity will experience a population growth of almost 30 %. The population in the tsunami inundation area is estimated to grow by more than 60 %. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Nat. Hazards Earth Syst. Sci., 25, 2271–2286, https://doi.org/10.5194/nhess-25-2271-2025, https://doi.org/10.5194/nhess-25-2271-2025, 2025
Short summary
Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
Julian Fäth, John Friesen, Andrea Sofia Garcia de León, Julia Rieder, Christian Schäfer, Tobias Leichtle, Tobias Ullmann, and Hannes Taubenböck
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-7-2025, 267–273, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-267-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-267-2025, 2025
John Friesen, Tobias Leichtle, and Hannes Taubenböck
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-7-2025, 7–12, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-7-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-7-2025, 2025
Elisabeth Schoepfer, Rodrigo Cienfuegos, Jörn Lauterjung, Torsten Riedlinger, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 25, 1163–1167, https://doi.org/10.5194/nhess-25-1163-2025, https://doi.org/10.5194/nhess-25-1163-2025, 2025
Monika Friedemann, Martin Mühlbauer, Fabian Henkel, Tabea Wilke, and Torsten Riedlinger
Abstr. Int. Cartogr. Assoc., 9, 14, https://doi.org/10.5194/ica-abs-9-14-2025, https://doi.org/10.5194/ica-abs-9-14-2025, 2025
Hugo Rosero-Velásquez, Mauricio Monsalve, Juan Camilo Gómez Zapata, Elisa Ferrario, Alan Poulos, Juan Carlos de la Llera, and Daniel Straub
Nat. Hazards Earth Syst. Sci., 24, 2667–2687, https://doi.org/10.5194/nhess-24-2667-2024, https://doi.org/10.5194/nhess-24-2667-2024, 2024
Short summary
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.
Ivan Kuznetsov, Benjamin Rabe, Alexey Androsov, Ying-Chih Fang, Mario Hoppmann, Alejandra Quintanilla-Zurita, Sven Harig, Sandra Tippenhauer, Kirstin Schulz, Volker Mohrholz, Ilker Fer, Vera Fofonova, and Markus Janout
Ocean Sci., 20, 759–777, https://doi.org/10.5194/os-20-759-2024, https://doi.org/10.5194/os-20-759-2024, 2024
Short summary
Short summary
Our research introduces a tool for dynamically mapping the Arctic Ocean using data from the MOSAiC experiment. Incorporating extensive data into a model clarifies the ocean's structure and movement. Our findings on temperature, salinity, and currents reveal how water layers mix and identify areas of intense water movement. This enhances understanding of Arctic Ocean dynamics and supports climate impact studies. Our work is vital for comprehending this key region in global climate science.
Christian Werthmann, Marta Sapena, Marlene Kühnl, John Singer, Carolina Garcia, Tamara Breuninger, Moritz Gamperl, Bettina Menschik, Heike Schäfer, Sebastian Schröck, Lisa Seiler, Kurosch Thuro, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 24, 1843–1870, https://doi.org/10.5194/nhess-24-1843-2024, https://doi.org/10.5194/nhess-24-1843-2024, 2024
Short summary
Short summary
Early warning systems (EWSs) promise to decrease the vulnerability of self-constructed (informal) settlements. A living lab developed a partially functional prototype of an EWS for landslides in a Medellín neighborhood. The first findings indicate that technical aspects can be manageable, unlike social and political dynamics. A resilient EWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality in a reduced-support scenario.
Alexey Androsov, Sven Harig, Natalia Zamora, Kim Knauer, and Natalja Rakowsky
Nat. Hazards Earth Syst. Sci., 24, 1635–1656, https://doi.org/10.5194/nhess-24-1635-2024, https://doi.org/10.5194/nhess-24-1635-2024, 2024
Short summary
Short summary
Two numerical codes are used in a comparative analysis of the calculation of the tsunami wave due to an earthquake along the Peruvian coast. The comparison primarily evaluates the flow velocity fields in flooded areas. The relative importance of the various parts of the equations is determined, focusing on the nonlinear terms. The influence of the nonlinearity on the degree and volume of flooding, flow velocity, and small-scale fluctuations is determined.
Christian Geiß, Jana Maier, Emily So, Elisabeth Schoepfer, Sven Harig, Juan Camilo Gómez Zapata, and Yue Zhu
Nat. Hazards Earth Syst. Sci., 24, 1051–1064, https://doi.org/10.5194/nhess-24-1051-2024, https://doi.org/10.5194/nhess-24-1051-2024, 2024
Short summary
Short summary
We establish a model of future geospatial population distributions to quantify the number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru, for the year 2035. Areas of high earthquake intensity will experience a population growth of almost 30 %. The population in the tsunami inundation area is estimated to grow by more than 60 %. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.
Marta Sapena, Moritz Gamperl, Marlene Kühnl, Carolina Garcia-Londoño, John Singer, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 23, 3913–3930, https://doi.org/10.5194/nhess-23-3913-2023, https://doi.org/10.5194/nhess-23-3913-2023, 2023
Short summary
Short summary
A new approach for the deployment of landslide early warning systems (LEWSs) is proposed. We combine data-driven landslide susceptibility mapping and population maps to identify exposed locations. We estimate the cost of monitoring sensors and demonstrate that LEWSs could be installed with a budget ranging from EUR 5 to EUR 41 per person in Medellín, Colombia. We provide recommendations for stakeholders and outline the challenges and opportunities for successful LEWS implementation.
Juan Camilo Gómez Zapata, Massimiliano Pittore, Nils Brinckmann, Juan Lizarazo-Marriaga, Sergio Medina, Nicola Tarque, and Fabrice Cotton
Nat. Hazards Earth Syst. Sci., 23, 2203–2228, https://doi.org/10.5194/nhess-23-2203-2023, https://doi.org/10.5194/nhess-23-2203-2023, 2023
Short summary
Short summary
To investigate cumulative damage on extended building portfolios, we propose an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are being constantly developed by experts from various research fields to be used within a multi-risk context. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios.
Dirsa Feliciano, Orlando Arroyo, Tamara Cabrera, Diana Contreras, Jairo Andrés Valcárcel Torres, and Juan Camilo Gómez Zapata
Nat. Hazards Earth Syst. Sci., 23, 1863–1890, https://doi.org/10.5194/nhess-23-1863-2023, https://doi.org/10.5194/nhess-23-1863-2023, 2023
Short summary
Short summary
This article presents the number of damaged buildings and estimates the economic losses from a set of earthquakes in Sabana Centro, a region of 11 towns in Colombia.
Juan Camilo Gomez-Zapata, Nils Brinckmann, Sven Harig, Raquel Zafrir, Massimiliano Pittore, Fabrice Cotton, and Andrey Babeyko
Nat. Hazards Earth Syst. Sci., 21, 3599–3628, https://doi.org/10.5194/nhess-21-3599-2021, https://doi.org/10.5194/nhess-21-3599-2021, 2021
Short summary
Short summary
We present variable-resolution boundaries based on central Voronoi tessellations (CVTs) to spatially aggregate building exposure models and physical vulnerability assessment. Their geo-cell sizes are inversely proportional to underlying distributions that account for the combination between hazard intensities and exposure proxies. We explore their efficiency and associated uncertainties in risk–loss estimations and mapping from decoupled scenario-based earthquakes and tsunamis in Lima, Peru.
L. Petry, T. Meiers, D. Reuschenberg, S. Mirzavand Borujeni, J. Arndt, L. Odenthal, T. Erbertseder, H. Taubenböck, I. Müller, E. Kalusche, B. Weber, J. Käflein, C. Mayer, G. Meinel, C. Gengenbach, and H. Herold
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4-W1-2021, 89–96, https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-89-2021, https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-89-2021, 2021
Cited articles
Aguilar, Z., Lazares, F., Alarcon, S., Quispe, S., Uriarte, R., and Calderon, D.: Actualización de la Microzonificación Sísmica de la ciudad de Lima, International Symposium for CISMID 25th Anniversary 17–18 August, 2012, Lima, Peru, 2013.
Allen, T. I. and Wald, D. J.: Topographic Slope as a Proxy for Seismic Site-Conditions (VS30) and Amplification Around the Globe, Open-File Report, Geological Survey (U. S.), https://doi.org/10.3133/ofr20071357, 2007.
Androsov, A., Harig, S., Zamora, N., Knauer, K., and Rakowsky, N.: Nonlinear processes in tsunami simulations for the Peruvian coast with focus on Lima and Callao, Nat. Hazards Earth Syst. Sci., 24, 1635–1656, https://doi.org/10.5194/nhess-24-1635-2024, 2024.
Aravena Pelizari, P., Geiß, C., Aguirre, P., Santa María, H., Merino Peña, Y., and Taubenböck, H.: Automated building characterization for seismic risk assessment using street-level imagery and deep learning, ISPRS J. Photogramm., 180, 370–386, https://doi.org/10.1016/j.isprsjprs.2021.07.004, 2021.
Aristizábal, C., Bard, P.-Y., Beauval, C., and Gómez, J. C.: Integration of Site Effects into Probabilistic Seismic Hazard Assessment (PSHA): A Comparison between Two Fully Probabilistic Methods on the Euroseistest Site, Geosciences, 8, 285, https://doi.org/10.3390/geosciences8080285, 2018.
Bano, M. and Zowghi, D.: A Systematic Review on the Relationship between User Involvement and System Success, Inform. Software Tech., 58, 148–169, https://doi.org/10.1016/j.infsof.2014.06.011, 2014.
Bano, M., Zowghi, D., and da Rimini, F.: User Satisfaction and System Success: An Empirical Exploration of User Involvement in Software Development, Empir. Softw. Eng., 22, 2339–2372, https://doi.org/10.1007/s10664-016-9465-1, 2017.
Barquet, K., Englund, M., Inga, K., André, K., and Segnestam, L.: Conceptualizing Multiple Hazards and Cascading Effects on Critical Infrastructures, Disasters, 48, e12591, https://doi.org/10.1111/disa.12591, 2023.
Brinckmann, N., Pittore, M., Rüster, M., Proß, B., and Gomez-Zapata, J. C.: Put your models in the web – less painful, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8671, https://doi.org/10.5194/egusphere-egu2020-8671, 2020.
Brinckmann, N., Gomez-Zapata, J. C., Pittore, M., and Rüster, M.: DEUS: Damage-Exposure-Update-Service. V. 1.0, GFZ Data Services [code], https://doi.org/10.5880/riesgos.2021.011, 2021.
Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O'Rourke, T. D., Reinhorn, A. M., Shinozuka, M., Tierney, K., Wallace, W. A., and von Winterfeldt, D.: A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities, Earthq. Spectra, 19, 733–752, https://doi.org/10.1193/1.1623497, 2003.
Cardona, O. D., Ordaz, M. G., Reinoso, E., Yamín, L. E., and Barbat, A. H.: CAPRA – Comprehensive Approach to Probabilistic Risk Assessment: International Initiative for Risk Management Effectiveness, in: 15th World Conference on Earthquake Engineering, Lisbon, Portugal, 24–28 September 2012, https://www.researchgate.net/publication/259598259 (last access: 5 December 2024), 2012.
Ceferino, L., Kiremidjian, A., and Deierlein, G.: Regional Multiseverity Casualty Estimation Due to Building Damage following a Mw 8.8 Earthquake Scenario in Lima, Peru, Earthq. Spectra, 34, 1739–1761, https://doi.org/10.1193/080617EQS154M, 2018.
CENEPRED: Escenario de riesgo por sismo y tsunami para Lima Metropolitana y la provincia constitucional del Callao, https://dimse.cenepred.gob.pe/er/sismos/ESCENARIO-SISMO-TSUNAMI-LIMA-CALLAO.pdf (last access: 20 May 2024), 2017.
CERESIS: Catalogue for South America and the Caribbean prepared in the framework of GSHAP, Lima, Peru, http://www.seismo.ethz.ch/static/gshap/ceresis (last access: 6 December 2024), 1995.
Chiu, G. L. F. and Chock, G. Y. K.: Multihazard Performance Based Objective Design for Managing Natural Hazards Damage, IFAC Proceedings Volumes, 31, 159–164, https://doi.org/10.1016/S1474-6670(17)38490-2, 1998.
Ciurean, R., Gill, J., Reeves, H.J., O'Grady, S., Aldridge, T., Donald, K., Banks, V., Dankers, R., and Cole, S.: Review of multi-hazards research and risk assessments: British Geological Survey Open Report, OR/18/057, 86 pp., https://nora.nerc.ac.uk/id/eprint/524399/1/OR18057.pdf (last access: 6 December 2024), 2018.
COES: Portal Web del COES, https://www.coes.org.pe/portal/ (last access: 20 May 2024), 2019.
Cremen, G., Galasso, C., and McCloskey, J.: Modelling and quantifying tomorrow's risks from natural hazards, Sci. Total Environ., 817, 152552, https://doi.org/10.1016/j.scitotenv.2021.152552, 2022.
Crucitti, P., Latora, V., and Marchiori, M.: Model for cascading failures in complex networks, Phys. Rev. E, 69, 045104, https://doi.org/10.1103/PhysRevE.69.045104, 2004.
Curt, C.: Multirisk: What trends in recent works? – A bibliometric analysis, Sci. Total Environ., 763, 142951, https://doi.org/10.1016/j.scitotenv.2020.142951, 2021.
De Angeli, S., Malamud, B. D., Rossi, L., Taylor, F. E., Trasforini, E., and Rudari, R.: A multi-hazard framework for spatial-temporal impact analysis, Int. J. Disast. Risk Re., 73, 102829, https://doi.org/10.1016/j.ijdrr.2022.102829, 2022.
De Pippo, T., Donadio, C., Pennetta, M., Petrosino, C., Terlizzi, F., and Valente, A.: Coastal hazard assessment and mapping in Northern Campania, Italy, Geomorphology, 97, 451–466, https://doi.org/10.1016/j.geomorph.2007.08.015, 2008.
European Commission: DRMKC Disaster Risk Management Knowledge Centre, https://drmkc.jrc.ec.europa.eu/ (last access: 20 May 2024), 2023.
European Commission: Ethics in Social Science and Humanities, https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ethics-in-social-science-and-humanities_he_en.pdf (last access: 20 May 2024), 2021.
EEA: Europe's changing climate hazards – an index-based interactive EEA report, https://www.eea.europa.eu/publications/europes-changing-climate-hazards-1 (last access: 20 May 2024), 2021.
Ferrario, E., Poulos, A., Castro, S., Llera, J. C. de la, and Lorca, A.: Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks, Reliab. Eng. Syst. Safe., 217, 108040, https://doi.org/10.1016/j.ress.2021.108040, 2022.
Fitz Simons, L. N.: Multi-Hazard Assessment of Localities and Sites, in: Protecting Historic Architecture and Museum Collections from Natural Disasters, edited by: Jones, B. G., Butterworth-Heinemann, 119–129, https://doi.org/10.1016/B978-0-409-90035-4.50014-5, 1986.
FEMA: HAZUS® MH Risk Assessment and User Group Series, Using HAZUS-MH for Risk Assessment, How-to Guide, 2004, https://www.fema.gov/pdf/plan/prevent/hazus/fema433.pdf (last access: 20 May 2024), 2004.
FEMA: HAZUS®MH MR4 Technical Manual, Multi-hazard Loss Estimation Methodology, Earthquake model, Department of Homeland Security, Emergency Preparedness and Response Directorate FEMA, https://de.scribd.com/document/281758929/Hazus-Mr4-Earthquake-Tech-Manual (last access: 6 December 2024), 2003.
FIUBA: Estudio del evento ocurrido el 16 de junio de 2019 en instalaciones del SADI, Comportamiento de los agentes del Mercado en el evento, Informe final, Buenos Aires: Universidad de Buenos Aires, Facultad de Ingeniería, Departamento de Energía, 236 pp., https://www.enre.gov.ar/web/bibliotd.nsf/203df3042bad9c40032578f6004ed613/d50f985c66ad65cd032586d9004ccbd1/$FILE/INFORME_FIUBA-version2%20rev26.pdf (last access: 6 December 2024), 2020.
Frigerio, S. and Westen, C. J.: RiskCity and WebRiskCity: Data Collection, Display, and Dissemination in a Multi-Risk Training Package, Cartogr. Geogr. Inf. Sc., 37, 119–135, https://doi.org/10.1559/152304010791232190, 2010.
Frimberger, T., Andrade, S. D., Weber, S., and Krautblatter, M.: Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar, Earth Surf. Proc. Land., 46, 680–700, https://doi.org/10.1002/esp.5056, 2021.
Fuchs, S., Frazier, T., and Siebeneck, L.: Physical Vulnerability: Vulnerability and Resilience to Natural Hazards, edited by: Fuchs, S. and Thaler, T., Cambridge University Press, https://doi.org/10.1017/9781316651148, 2018.
Gallina, V., Torresan, S., Critto, A., Sperotto, A., Glade, T., and Marcomini, A.: A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment, J. Environ. Manage., 168, 123–132, https://doi.org/10.1016/j.jenvman.2015.11.011, 2016.
Geiß, C. and Taubenböck, H.: Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap, Nat. Hazards, 68, 7–48, https://doi.org/10.1007/s11069-012-0322-2, 2013.
Geiß, C., Taubenböck, H., Tyagunov, S., Tisch, A., Post, J., and Lakes, T.: Assessment of Seismic Building Vulnerability from Space, Earthq. Spectra, 30, 1553–1583, https://doi.org/10.1193/121812EQS350M, 2014.
Geiß, C., Aravena Pelizari, P., Marconcini, M., Sengara, W., Edwards, M., Lakes, T., and Taubenböck, H.: Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques, ISPRS J. Photogramm., 104, 175–188, https://doi.org/10.1016/j.isprsjprs.2014.07.016, 2015.
Geiß, C., Thoma, M., Pittore, M., Wieland, M., Dech, S. W., and Taubenböck, H.: Multitask Active Learning for Characterization of Built Environments with Multisensor Earth Observation Data, IEEE J. Sel. Top. Appl., 10, 5583–5597, https://doi.org/10.1109/JSTARS.2017.2748339, 2017.
Geiß, C., Pelizari, P., Bauer, S., Schmitt, A., and Taubenböck, H.: Automatic Training Set Compilation With Multisource Geodata for DTM Generation From the TanDEM-X DSM, in: IEEE Geoscience and Remote Sensing Letters, vol. 17, 456–460, https://doi.org/10.1109/LGRS.2019.2921600, 2019.
Geiß, C., Priesmeier, P., Aravena Pelizari, P., Soto Calderon, A. R., Schoepfer, E., Riedlinger, T., Villar Vega, M., Santa María, H., Gómez Zapata, J. C., Pittore, M., So, E., Fekete, A., and Taubenböck, H.: Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile, Nat. Hazards, 119, 779–804, https://doi.org/10.1007/s11069-022-05672-6, 2022.
GEM: Report on the SARA Exposure and Vulnerability Workshop in Medellín, Report produced in the context of the GEM South America integrated Risk Assessment (SARA) project No. Version 1.0-May 2014, Colombia, 2014.
GEM Secretariat: South America Risk Assessment (SARA) Final Report 2015, Version 1.0., December 2015. SARA Project, https://cloud-storage.globalquakemodel.org/public/wix-new-website/pdf-collections-wix/publications/SARA%20Final%20Report_Exec%20Summary.pdf (last access: 20 May 2024), 2015.
Greiving, S., Fleischhauer, M., León, C. D., Schödl, L., Wachinger, G., Quintana Miralles, I. K., and Prado Larraín, B.: Participatory Assessment of Multi Risks in Urban Regions – The Case of Critical Infrastructures in Metropolitan Lima, Sustainability, 13, 2813, https://doi.org/10.3390/su13052813, 2021.
Gill, J. C. and Malamud, B. D.: Reviewing and visualizing the interactions of natural hazards, 52, 680–722, https://doi.org/10.1002/2013RG000445, 2014.
Gill, J. C. and Malamud, B. D.: Hazard interactions and interaction networks (cascades) within multi-hazard methodologies, Earth Syst. Dynam., 7, 659–679, https://doi.org/10.5194/esd-7-659-2016, 2016.
Gill, J. C. and Malamud, B. D.: Anthropogenic processes, natural hazards, and interactions in a multi-hazard framework, Earth-Sci. Rev., 166, 246–269, https://doi.org/10.1016/j.earscirev.2017.01.002, 2017.
Gill, J. C., Malamud, B. D., Barillas, E. M., and Guerra Noriega, A.: Construction of regional multi-hazard interaction frameworks, with an application to Guatemala, Nat. Hazards Earth Syst. Sci., 20, 149–180, https://doi.org/10.5194/nhess-20-149-2020, 2020.
Goda, K. and De Risi, R.: Future perspectives of earthquake-tsunami catastrophe modelling: From single-hazards to cascading and compounding multi-hazards, Front. Built Environ., 8, https://doi.org/10.3389/fbuil.2022.1022736, 2023.
Gómez Zapata, J. C. and Pittore, M.: Probabilistic inter-scheme compatibility matrices for buildings. An application using existing vulnerability models for earthquakes and tsunami from synthetic datasets constructed using the AeDEs form through expert-based heuristics, GFZ Data Services [data set], https://doi.org/10.5880/riesgos.2022.003, 2022.
Gómez Zapata, J. C., Parrado, C., Frimberger, T., Barragán-Ochoa, F., Brill, F., Büche, K., Krautblatter, M., Langbein, M., Pittore, M., Rosero-Velásquez, H., Schoepfer, E., Spahn, H., and Zapata-Tapia, C.: Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador, Sustainability, 13, 1714, https://doi.org/10.3390/su13041714, 2021a.
Gomez-Zapata, J. C., Brinckmann, N., Harig, S., Zafrir, R., Pittore, M., Cotton, F., and Babeyko, A.: Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment: an application case in Lima, Peru, Nat. Hazards Earth Syst. Sci., 21, 3599–3628, https://doi.org/10.5194/nhess-21-3599-2021, 2021b.
Gómez Zapata, J. C., Cotton, F., and Brinckmann, N.: Seismic ground motion fields for six deterministic earthquake scenarios (Mw 8.5-9.0) for Lima (Peru), GFZ Data Services [data set], https://doi.org/10.5880/riesgos.2021.008, 2021c.
Gómez Zapata, J. C., Shinde, S., Pittore, M., and Merino-Peña, Y.: Scripts to generate (1) attribute-based fuzzy scores for SARA and HAZUS building classes, and (2) probabilistic inter-scheme compatibility matrices. An application on the residential building stock of Valparaiso (Chile) for seismic risk applications, GFZ Data Services [code], https://doi.org/10.5880/riesgos.2021.002, 2021d.
Gómez Zapata, J. C., Zafrir, R., Harig, S., and Pittore, M.: Customised focus maps and resultant CVT-based aggregation entities for Lima and Callao (Peru), V. 1.0, GFZ Data Services [data], https://doi.org/10.5880/riesgos.2021.006 (last access: 6 December 2024), 2021e.
Gómez Zapata, J. C., Zafrir, R., Brinckmann, N., and Pittore, M.: Residential building exposure and physical vulnerability models for ground-shaking and tsunami risk in Lima and Callao (Peru), V. 1.0, GFZ Data Services [data], https://doi.org/10.5880/riesgos.2021.007, 2021f.
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. Earthq. Eng., 20, 2401–2438, https://doi.org/10.1007/s10518-021-01312-9, 2022a.
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. Geo-Inf., 11, 113, https://doi.org/10.3390/ijgi11020113, 2022b.
Gómez Zapata, J. C., Medina, S., and Lizarazo-Marriaga, J.: Creation of simplified state-dependent fragility functions through ad-hoc scaling factors to account for previous damage in a multi-hazard risk context. An application to flow-depth-based analytical tsunami fragility functions for the Pacific coast of South America, GFZ Data Service [data set], https://doi.org/10.5880/riesgos.2022.002, 2022c.
Gómez Zapata, J. C., Pittore, M., Brinckmann, N., Lizarazo-Marriaga, J., Medina, S., Tarque, N., and Cotton, F.: Scenario-based multi-risk assessment from existing single-hazard vulnerability models. An application to consecutive earthquakes and tsunamis in Lima, Peru, Nat. Hazards Earth Syst. Sci., 23, 2203–2228, https://doi.org/10.5194/nhess-23-2203-2023, 2023.
Gomillion, D. L.: The Co-Creation of Information Systems, PhD thesis, Florida State University, USA, http://purl.flvc.org/fsu/fd/FSU_migr_etd-7396 (last access: 6 December 2024), 2013.
Gould, J. D. and Lewis, C.: Designing for usability: key principles and what designers think, Commun. ACM, 28, 300–311, https://doi.org/10.1145/3166.3170, 1985.
Granger, K., Jones, T., Leiba, M., and Scott, G.: Community risk in Cairns: A multi-hazard risk assessment, Australian Journal of Emergency Management, 14, 29–30, 1999.
Giuliani, G. and Peduzzi, P.: The PREVIEW Global Risk Data Platform: a geoportal to serve and share global data on risk to natural hazards, Nat. Hazards Earth Syst. Sci., 11, 53–66, https://doi.org/10.5194/nhess-11-53-2011, 2011.
Harig, S. and Rakowsky, N.: Tsunami flow depth in Lima/Callao (Peru) caused by six hypothetical simplified tsunami scenarios offshore Lima, GFZ Data Services [data set], https://doi.org/10.5880/riesgos.2021.010, 2021.
Harig, S., Chaeroni, Pranowo, W. S., and Behrens, J.: Tsunami simulations on several scales, Ocean Dynam., 58, 429–440, https://doi.org/10.1007/s10236-008-0162-5, 2008.
Harig, S., Immerz, A., Weniza, Griffin, J., Weber, B., Babeyko, A., Rakowsky, N., Hartanto, D., Nurokhim, A., Handayani, T., and Weber, R.: The Tsunami Scenario Database of the Indonesia Tsunami Early Warning System (InaTEWS): Evolution of the Coverage and the Involved Modeling Approaches, Pure Appl. Geophys., 177, 1379–1401, https://doi.org/10.1007/s00024-019-02305-1, 2020.
Harig, S., Zamora, N., Androsov, A., and Knauer, K.: Tsunami flow depth in Lima/Callao caused by a historic event for varying bottom roughness simulated with the models Tsunami-HySEA and TsunAWI, GFZ Data Services [data set], https://doi.org/10.5880/riesgos.2024.001, 2024.
He, J. and King, W.: The Role of User Participation in Information Systems Development: Implications from a Meta-Analysis, J. Manage. Inform. Syst., 25, 301–331, https://doi.org/10.2753/MIS0742-1222250111, 2008.
Hernandez-Fajardo, I. and Dueñas-Osorio, L.: Probabilistic study of cascading failures in complex interdependent lifeline systems, Reliab. Eng. Syst. Safe., 111, 260–272, https://doi.org/10.1016/j.ress.2012.10.012, 2013.
Hochrainer-Stigler, S., Šakić Trogrlić, R., Reiter, K., Ward, P. J., de Ruiter, M. C., Duncan, M. J., Torresan, S., Ciurean, R., Mysiak, J., Stuparu, D., and Gottardo, S.: Toward a framework for systemic multi-hazard and multi-risk assessment and management, iScience, 26, 106736, https://doi.org/10.1016/j.isci.2023.106736, 2023.
Hossain, S., Spurway, K., Zwi, A. B., Huq, N. L., Mamun, R., Islam, R., Nowrin, I., Ether, S., Bonnitcha, J., Dahal, N., and Adams, A. M.: What is the impact of urbanisation on risk of, and vulnerability to, natural disasters? What are the effective approaches for reducing exposure of urban population to disaster risks? London: EPPI Centre, Social Science Research Unit, UCL Institute of Education, University College London, https://eppi.ioe.ac.uk/cms/Default.aspx?tabid=3707 (last access: 20 May 2024), 2017.
INDECI: Escenario sísmico para Lima Metropolitana y Callao: Sismo 8.8 Mw, CEPIG, https://portal.indeci.gob.pe/wp-content/uploads/2019/01/201711231521471-1.pdf, last access: 20 May 2024), 2017.
INDECI: Compendio estadístico del INDECI 2020. En la Preparación, Respuesta y Rehabilitiación de la GRD. Información estadística de emergencias y danos, period 2003 al 2019, https://cdn.www.gob.pe/uploads/document/file/1689973/CAPITULO%20III.%20Estad%C3%ADstica%20Series%202003-2019.pdf?v=1614182435 (last access: 20 May 2024), 2020.
INEI: Censos Nacionales 2017, Instituto Nacional de Estadistica e Informatica (INEI; Institute of Statistic and Informatics), Lima, Peru, 2017.
INEI: Sistema estadístico nacional: Perú Compendio Estadístico 2022, https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1872/COMPENDIO2022.html (last access: 20 May 2024), 2022.
Intergovernmental Oceanographic Commission: Tsunami Glossary, Fourth Edition, Paris, UNESCO, IOC Technical Series, 85, (English, French, Spanish, Arabic, Chinese), IOC/2008/TS/85 rev.4, https://unesdoc.unesco.org/ark:/48223/pf0000188226?posInSet=1&queryId=aeb846ae-edfb-4d66-a03a-385a5d5897f0 (last access: 6 December 2024), 2019.
Jarke, M., Bui, T., and Carroll, J.: Scenario Management: An Interdisciplinary Approach, Requir. Eng., 3, 155–173, https://doi.org/10.1007/s007660050002, 1998.
Jimenez, C., Moggiano, N., Mas, E., Adriano, B., Koshimura, S., Fujii, Y., and Yanagisawa, H.: Seismic Source of 1746 Callao Earthquake from Tsunami Numerical Modeling, Journal of Disaster Research, 8, 266–273, 2013.
Joint Research Centre (European Commission), Luoni, S., Antofie, T. E., Eklund, L. G., and Marín Ferrer, M.: Update of risk data hub software and data architecture: software solutions for disaster risk management, Publications Office of the European Union, LU, https://doi.org/10.2760/798003, 2020.
Kappes, M., Keiler, M., and Glade, T.: From Single- to Multi-Hazard Risk analyses: a concept addressing emerging challenges, Mountain risks: Bringing science to society, Proceedings of the international conference, Florence, 24–26 November 2010, 351–356, https://www.researchgate.net/publication/260692785_From_Single-_to_Multi-Hazard_Risk_analyses_a_concept_addressing_emerging_challenges (last access: 6 December 2024), 2010.
Kappes, M. S., Keiler, M., von Elverfeldt, K., and Glade, T.: Challenges of analyzing multi-hazard risk: a review, Nat. Hazards, 64, 1925–1958, https://doi.org/10.1007/s11069-012-0294-2, 2012.
Karat, J.: Evolving the scope of user-centered design, Commun. ACM, 40, 33–38, https://doi.org/10.1145/256175.256181, 1997.
Kent, B., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., and Thomas, D.: Manifesto for Agile Software Development, Agile Alliance, https://agilemanifesto.org/ (last access: 20 May 2024), 2001.
Kling, R.: The Organizational Context of User-Centered Software Designs, MIS Quart., 1, 41–52, https://doi.org/10.2307/249021, 1977.
Komendantova, N., Mrzyglocki, R., Mignan, A., Khazai, B., Wenzel, F., Patt, A., and Fleming, K.: Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: Feedback from civil protection stakeholders, Int. J. Disast. Risk Re., 8, 50–67, https://doi.org/10.1016/j.ijdrr.2013.12.006, 2014.
Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M., Younis, M., and Zink, M.: TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry, IEEE T. Geosci. Remote, 45, 3317–3341, https://doi.org/10.1109/TGRS.2007.900693, 2007.
Kropf, C. M., Ciullo, A., Otth, L., Meiler, S., Rana, A., Schmid, E., McCaughey, J. W., and Bresch, D. N.: Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0, Geosci. Model Dev., 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022, 2022.
Kulikov, E. A., Rabinovich, A. B., and Thomson, R. E.: Estimation of Tsunami Risk for the Coasts of Peru and Northern Chile, Nat. Hazards, 35, 185–209, https://doi.org/10.1007/s11069-004-4809-3, 2005.
Kujala, S.: User involvement: A review of the benefits and challenges, Behav. Inform. Technol., 22, 1–16, https://doi.org/10.1080/01449290301782, 2003.
Langbein, M., Boeck, M., and Mandery, N.: riesgos/dlr-riesgos-frontend: 2.0.6-peru, Zenodo [code], https://doi.org/10.5281/zenodo.8024669, 2023.
Leyton, F., Ruiz, S., and Sepúlveda, S. A.: Preliminary re-evaluation of probabilistic seismic hazard assessment in Chile: from Arica to Taitao Peninsula, Adv. Geosci., 22, 147–153, https://doi.org/10.5194/adgeo-22-147-2009, 2009.
Li, M., Wang, J., and Sun, X.: Scenario-based risk framework selection and assessment model development for natural disasters: a case study of typhoon storm surges, Nat. Hazards, 80, 2037–2054, https://doi.org/10.1007/s11069-015-2059-1, 2016.
Liu, Z., Nadim, F., Garcia-Aristizabal, A., Mignan, A., Fleming, K., and Luna, B. Q.: A three-level framework for multi-risk assessment, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 9, 5974, https://doi.org/10.1080/17499518.2015.1041989, 2015.
Liu, B., Siu, Y. L., and Mitchell, G.: Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment, Nat. Hazards Earth Syst. Sci., 16, 629–642, https://doi.org/10.5194/nhess-16-629-2016, 2016.
López-Saavedra, M. and Martí, J.: Reviewing the multi-hazard concept, Application to volcanic islands, Earth-Sci. Rev., 236, 104286, https://doi.org/10.1016/j.earscirev.2022.104286, 2023.
Marin Ferrer, M., Antofie, T., Eklund, G., and Luoni, S.: The Disaster Risk Management Knowledge Center – Risk Data Hub: Vision Paper & roadmap, European Commission, Ispra, JRC119384, https://drmkc.jrc.ec.europa.eu/doc/18150 (last access: 6 December 2024), 2019.
Marzocchi W., Mastellone M. L., Di Ruocco A., Novelli P., Romeo E., and Gasparini P.: Principles of multi-risk assessment: interaction amongst natural and man-induced risks (Project report), Office for Official Publications of the European Communities, Luxembourg, https://doi.org/10.2777/30886, 2009.
Meaux, A. and Osofisan, W.: A review of context analysis tools for urban humanitarian response, https://www.iied.org/10797iied (last access: 20 May 2024), 2016.
Medina, S.: Zonificación de la vulnerabilidad física para edificaciones típicas en San Andrés de Tumaco, Costa Pacífica Colombiana, Master thesis in Civil Engineering, Universidad Nacional de Colombia Facultad de Ingeniería, Departamento Ingeniería Civil y Ambiental, Bogotá, Colombia, 245 pp., https://repositorio.unal.edu.co/handle/unal/77178 (last access: 6 December 2024), 2019.
Medina, S., Lizarazo-Marriaga, J., Estrada, M., Koshimura, S., Mas, E., and Adriano, B.: Tsunami analytical fragility curves for the Colombian Pacific coast: A reinforced concrete building example, Eng. Struct., 196, 109309, https://doi.org/10.1016/j.engstruct.2019.109309, 2019.
Merscher, C.: Seismic and Tsunami Hazard Analysis and cascading Effects to the Power Network in Lima and Callao, Peru. Master's thesis, Technical University of Munich, Germany, 2020.
Mignan, A., Wiemer, S., and Giardini, D.: The quantification of low-probability–high-consequences events: part I. A generic multi-risk approach, Nat. Hazards, 73, 1999–2022, https://doi.org/10.1007/s11069-014-1178-4, 2014.
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.
Munich RE: Relevant natural catastrophe loss events worldwide 2021, https://www.munichre.com/content/dam/munichre/mrwebsiteslaunches/natcat-2022/NatCat-Weltkarte-2021-1920x1080.pdf/_jcr_content/renditions/original./NatCat-Weltkarte-2021-1920x1080.pdf (last access: 20 May 2024), 2022.
Negulescu, C., Smai, F., Quique, R., Hohmann, A., Clain, U., Guidez, R., Tellez-Arenas, A., Quentin, A., and Grandjean, G.: VIGIRISKS platform, a web-tool for single and multi-hazard risk assessment, Nat. Hazards, 115, 593–618, https://doi.org/10.1007/s11069-022-05567-6, 2023.
Neri, A., Aspinall, W. P., Cioni, R., Bertagnini, A., Baxter, P. J., Zuccaro, G., Andronico, D., Barsotti, S., Cole, P. D., Esposti Ongaro, T., Hincks, T. K., Macedonio, G., Papale, P., Rosi, M., Santacroce, R., and Woo, G.: Developing an Event Tree for probabilistic hazard and risk assessment at Vesuvius, J. Volcanol. Geoth. Res., 178, 397–415, https://doi.org/10.1016/j.jvolgeores.2008.05.014, 2008.
Neri, M., Le Cozannet, G., Thierry, P., Bignami, C., and Ruch, J.: A method for multi-hazard mapping in poorly known volcanic areas: an example from Kanlaon (Philippines), Nat. Hazards Earth Syst. Sci., 13, 1929–1943, https://doi.org/10.5194/nhess-13-1929-2013, 2013.
Nievas, C. I., Bommer, J. J., Crowley, H., van Elk, J., Ntinalexis, M., and Sangirardi, M.: A database of damaging small-to-medium magnitude earthquakes, J Seismol, 24, 263–292, https://doi.org/10.1007/s10950-019-09897-0, 2020.
Norman, D. A. and Draper, W. (Eds.): User-Centered System Design: New Perspectives on Human-Computer Interaction, University of California, San Diego, Taylor and Francis, 526 pp., https://www.taylorfrancis.com/books/edit/10.1201/9780367807320/user-centered-system-design-donald-norman (last access: 6 December 2024), 1986.
Oberkampf, W. L., DeLand, S. M., Rutherford, B. M., Diegert, K. V., and Alvin, K. F.: Error and uncertainty in modeling and simulation, Reliab. Eng. Syst. Safe., 75, 333–357, https://doi.org/10.1016/S0951-8320(01)00120-X, 2002.
OECD (Organistation for Economic Co-operation and Development): Disaster Risk Assessment and Risk Financing A G20/OECD Methodological Framework, https://doi.org/10.1787/8f48d476-en, 2012.
Olarte, J., Aguilar, Z., Zavala, C., Martinez, A., and Gallardo, J.: Estimate of the probable maximum loss PML in Lima and Callao: Application to the Peruvian insurance industry, https://www.researchgate.net/publication/228758296_ESTIMATE_OF_THE_PROBABLE_MAXIMUM_LOSS_PML_IN_LIMA_AND_CALLAO_APPLICATION_TO_THE_PERUVIAN_INSURANCE_INDUSTRY (last access: 6 December 2024), 2008.
OSINERGMIN: Organismo Supervisor de la Inversión en Energía y Minería, https://www.gob.pe/osinergmin (last access: 20 May 2024), 2019.
Ouyang, M., Dueñas-Osorio, L., and Min, X.: A three-stage resilience analysis framework for urban infrastructure systems, Struct. Saf., 36–37, 23–31, https://doi.org/10.1016/j.strusafe.2011.12.004, 2012.
Owolabi, T. A. and Sajjad, M.: A global outlook on multi-hazard risk analysis: A systematic and scientometric review, Int. J. Disast. Risk Re., 92, 103727, https://doi.org/10.1016/j.ijdrr.2023.103727, 2023.
Pagani, M., Monelli, D., Weatherill, G., Danciu, L., Crowley, H., Silva, V., Henshaw, P., Butler, L., Nastasi, M., Panzeri, L., Simionato, M., and Vigano, D.: OpenQuake Engine: An Open Hazard (and Risk) Software for the Global Earthquake Model, Seismol. Res. Lett., 85, 692–702, https://doi.org/10.1785/0220130087, 2014.
Pagani, M., Johnson, K., and Garcia Pelaez, J.: Modelling subduction sources for probabilistic seismic hazard analysis, in: Characterization of Modern and Historical Seismic–Tsunamic Events, and Their Global–Societal Impacts, edited by: Dilek, Y., Ogawa, Y., and Okubo, Y., Geological Society of London, https://doi.org/10.1144/SP501-2019-120, 2021.
Pascale, S., Sdao, F., and Sole, A.: A model for assessing the systemic vulnerability in landslide prone areas, Nat. Hazards Earth Syst. Sci., 10, 1575–1590, https://doi.org/10.5194/nhess-10-1575-2010, 2010.
Paulik, R., Horspool, N., Woods, R., Griffiths, N., Beale, T., Magill, C., Wild, A., Popovich, B., Walbran, G., and Garlick, R.: RiskScape: a flexible multi-hazard risk modelling engine, Nat. Hazards, 199, 1073–1090, https://doi.org/10.1007/s11069-022-05593-4, 2022.
Pesaresi, M., Ehrlich, D., Kemper, T., Siragusa, A., Florczyk, A., Freire, S., and Corbane, C.: Atlas of the human planet 2017, Global exposure to natural hazards, Joint Research Centre (European Commission), Luxembourg: Publictations Office of the European Union, 2017, https://doi.org/10.2760/19837, 2017.
Pitilakis, K., Crowley, H., and Kaynia, A. M. (Eds.): SYNER-G: Typology Definition and Fragility Functions for Physical Elements at Seismic Risk: Buildings, Lifelines, Transportation Networks and Critical Facilities, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-7872-6, 2014.
Pittore, M., Wieland, M., and Fleming, K.: Perspectives on global dynamic exposure modelling for geo-risk assessment, Nat. Hazards, 86, 7–30, https://doi.org/10.1007/s11069-016-2437-3, 2017.
Pittore, M., Gómez Zapata, J. C., Brinckmann, N., Weatherill, G., Babeyko, A., Harig, S., Mahdavi, A., Proß, B., Rosero Velasquez, H. F., Straub, D., Krautblatter, M., Frimberger, T., Langbein, M., Geiß, C., and Schoepfer, E.: Towards an integrated framework for distributed, modular multi-risk scenario assessment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19097, https://doi.org/10.5194/egusphere-egu2020-19097, 2020.
Pittore, M., Haas, M., Gomez-Zapata, J. C., Brinckmann, N., Rüster, M., and Proß, B.: Quakeledger: a web service to serve earthquake scenarios. V. 1.0, GFZ Data Services [code], https://doi.org/10.5880/riesgos.2021.003, 2021a.
Pittore, M., Gomez-Zapata, J. C., 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, 2021b.
Plank, S., Nolde, M., Richter, R., Fischer, C., Martinis, S., Riedlinger, T., Schoepfer, E., and Klein, D.: Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR's Experimental TET-1 Satellite, Remote Sens., 10, 1379, https://doi.org/10.3390/rs10091379, 2018.
Rakowsky, N., Androsov, A., Fuchs, A., Harig, S., Immerz, A., Danilov, S., Hiller, W., and Schröter, J.: Operational tsunami modelling with TsunAWI – recent developments and applications, Nat. Hazards Earth Syst. Sci., 13, 1629–1642, https://doi.org/10.5194/nhess-13-1629-2013, 2013.
Rinaldi, S. M., Peerenboom, J. P., and Kelly, T. K.: Identifying, understanding, and analyzing critical infrastructure interdependencies, IEEE Contr. Syst. Mag., 21, 11–25, https://doi.org/10.1109/37.969131, 2001.
Rodríguez, E. E., Portner, D. E., Beck, S. L., Rocha, M. P., Bianchi, M. B., Assumpção, M., Ruiz, M., Alvarado, P., Condori, C., and Lynner, C.: Mantle dynamics of the Andean Subduction Zone from continent-scale teleseismic S -wave tomography, Geophys. J. Int., 224, 1553–1571, https://doi.org/10.1093/gji/ggaa536, 2020.
Rosero-Velásquez, H.: GitHub – riesgos/System_Reliability: WPS for performing the reliability of infrastructure networks, https://github.com/riesgos/System_Reliability (last access: 6 December 2024), 2020.
Rosero-Velásquez, H., Gómez Zapata, J. C. and Straub, D.: Comparative Assessment of Models of Cascading Failures in Power Networks Under Seismic Hazard, Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, 28 August–1 September 2022, 1897–1904, https://doi.org/10.3850/978-981-18-5183-4_S03-03-221-cd, 2022.
Rosero-Velásquez, H. and Straub, D.: Selection of representative natural hazard scenarios for engineering systems, Earthq. Eng. Struct. D., 51, 3680–3700, https://doi.org/10.1002/eqe.3743, 2022.
Rosero-Velásquez, H.: Graph-based model for reliability analysis of infrastructure networks (1.0), mediaTUM, Technical University Munich [data set], https://doi.org/10.14459/2024MP1735865, 2024.
Šakić Trogrlić, R., Donovan, A., and Malamud, B. D.: Invited perspectives: Views of 350 natural hazard community members on key challenges in natural hazards research and the Sustainable Development Goals, Nat. Hazards Earth Syst. Sci., 22, 2771–2790, https://doi.org/10.5194/nhess-22-2771-2022, 2022.
SARA: Research Topic 3 (RP3) – Modelling Subduction Zones in South America, https://sara.openquake.org/hazard_rt3/ (last access: 26 July 2023), 2016a.
SARA: Research Topic 4 (RT4) – The South American Earthquake Catalogue, https://sara.openquake.org/hazard_rt4 (last access: 26 July 2023), 2016b.
Schoepfer, E., Lauterjung, J., Kreibich, H., Rakowsky, N., Krautblatter, M., Straub, D., Stasch, C., Jäger, S., Knauer, K., Greiving, S., León, C., Spahn, H., and Riedlinger, T.: Research towards improved management of natural disasters including strategies to reduce cascading effects, in: EGU General Assembly 2018. Vienna, Austria, 8 April–13 Apr 2018, EGU2018-14801, https://elib.dlr.de/123317/1/EGU_2018_RIESGOS_poster-final.pdf (last access: 6 December 2024), 2018.
Schoepfer, E., Juzam, L., Lauterjung, J., León, C. D., Riedlinger, T., Spahn, H., and Zambrano, A.: Policy brief – Multi-risk analysis: What would happen if…?, DLR electronic library, 21 pp., https://doi.org/10.15489/cwgicmtcja61, 2024.
Schorlemmer, D., Euchner, F., Kästli, P., and Saul, J.: QuakeML: status of the XML-based seismological data exchange format, Ann. Geophys.-Italy, 54, 59–65, https://doi.org/10.4401/ag-4874, 2011.
Sedzro, K. S. A., Lamadrid, A. J., and Zuluaga, L. F.: Allocation of Resources Using a Microgrid Formation Approach for Resilient Electric Grids, IEEE T. Power Syst., 33, 2633–2643, https://doi.org/10.1109/TPWRS.2017.2746622, 2018.
Silva, V., Crowley, H., Pagani, M., Monelli, D., and Pinho, R.: Development of the OpenQuake engine, the Global Earthquake Model's open-source software for seismic risk assessment, Nat. Hazards, 72, 1409–1427, https://doi.org/10.1007/s11069-013-0618-x, 2014.
Sköld Gustafsson, V., Hjerpe, M., and Strandberg, G.: Construction of a national natural hazard interaction framework: The case of Sweden, iScience, 26, 106501, https://doi.org/10.1016/j.isci.2023.106501, 2023.
Strunz, G., Schöpfer, E., Geiß, C., Riedlinger, T., Lauterjung, J., and Spahn, H.: Multi-Risikobewertung in der Andenregion – Forschung, Entwicklung und praktische Anwendung, zfv – Zeitschrift für Geodäsie, Geoinformation und Landmanagement, zfv 1/2022, 63–70, https://doi.org/10.12902/zfv-0374-2021, 2022.
Suppasri, A., Mas, E., Charvet, I., Gunasekera, R., Imai, K., Fukutani, Y., Abe, Y., and Imamura, F.: Building damage characteristics based on surveyed data and fragility curves of the 2011 Great East Japan tsunami, Nat. Hazards, 66, 319–341, https://doi.org/10.1007/s11069-012-0487-8, 2013.
Sutcliffe, A.: Scenario-Based Requirements Engineering, IEEE T. Software Eng., 320–329, https://doi.org/10.1109/ICRE.2003.1232776, 2003.
Tarvainen, T., Jarva, J., and Greiving, S.: Spatial pattern of hazards and hazard interactions in Europe, Geological Survey of Finnland, Special Paper 42, Espoo 2006, 83–91, https://www.researchgate.net/publication/238538264_Development_of_Natural_Hazard_maps_for_European_Regions (last access: 12 December 2024), 2006.
Taubenböck, H., Post, J., Roth, A., Zosseder, K., Strunz, G., and Dech, S.: A conceptual vulnerability and risk framework as outline to identify capabilities of remote sensing, Nat. Hazards Earth Syst. Sci., 8, 409–420, https://doi.org/10.5194/nhess-8-409-2008, 2008.
Taubenböck, H., Goseberg, N., Setiadi, N., Lämmel, G., Moder, F., Oczipka, M., Klüpfel, H., Wahl, R., Schlurmann, T., Strunz, G., Birkmann, J., Nagel, K., Siegert, F., Lehmann, F., Dech, S., Gress, A., and Klein, R.: “Last-Mile” preparation for a potential disaster – Interdisciplinary approach towards tsunami early warning and an evacuation information system for the coastal city of Padang, Indonesia, Nat. Hazards Earth Syst. Sci., 9, 1509–1528, https://doi.org/10.5194/nhess-9-1509-2009, 2009.
Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., and Dech, S.: Monitoring urbanization in mega cities from space, Remote Sens. Environ., 117, 162–176, https://doi.org/10.1016/j.rse.2011.09.015, 2012.
Taubenböck, H., Goseberg, N., Lämmel, G., Setiadi, N., Schlurmann, T., Nagel, K., Siegert, F., Birkmann, J., Traub, K.-P., Dech, S., Keuck, V., Lehmann, F., Strunz, G., and Klüpfel, H.: Risk reduction at the “Last-Mile”: an attempt to turn science into action by the example of Padang, Indonesia, Nat. Hazards, 65, 915–945, https://doi.org/10.1007/s11069-012-0377-0, 2013.
Tavera, H., Agüero, C., Fernandez, E., and Rodriguez, S.: Catálogo Sísmico del Perú 1471 – 1982 Versión Revisada y Actualizada. Instituto Geofísico del Perú, Direccion de Sismologia, Lima, 2001, https://repositorio.igp.gob.pe/handle/20.500.12816/789 (last access: 20 May 2024), 2001.
Tilloy, A., Malamud, B. D., Winter, H., and Joly-Laugel, A.: A review of quantification methodologies for multi-hazard interrelationships, Earth-Sci. Rev., 196, 102881, https://doi.org/10.1016/j.earscirev.2019.102881, 2019.
Tilloy, A., Malamud, B. D., and Joly-Laugel, A.: A methodology for the spatiotemporal identification of compound hazards: wind and precipitation extremes in Great Britain (1979–2019), Earth Syst. Dynam., 13, 993–1020, https://doi.org/10.5194/esd-13-993-2022, 2022.
UCTE: Final Report of the Investigation Committee on the 28 September 2003 Blackout in Italy, UCTE Report, April 2004, https://eepublicdownloads.entsoe.eu/clean-documents/pre2015/publications/ce/otherreports/20040427_UCTE_IC_Final_report.pdf (last access: 6 December 2024), 2004.
UNDRR: Global Assessment Report on Disaster Risk Reduction 2019, Geneva, Switzerland, United Nations Office for Disaster Risk Reduction (UNDRR), ISBN 978-92-1-004180-5, https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2019 (last access: 6 December 2024), 2019.
UNDRR: Global Assessment Report on Disaster Risk Reduction 2022, Geneva, Switzerland, United Nations Office for Disaster Risk Reduction (UNDRR), ISBN 9789212320281, https://www.undrr.org/media/79595/download?startDownload=20241206 (last access: 6 December 2024), 2022a.
UNDRR: Hazard definition and classification review. Technical Report, https://www.undrr.org/publication/hazard-definition-and-classification-review-technical-report (last access: 20 May 2024), 2022b.
UNDRR: GAR Special Report 2023: Mapping resilience for the sustainable development goals, Geneva, Switzerland, United Nations Office for Disaster Risk Reduction (UNDRR), ISBN 9789210028301, http://www.undrr.org/gar2023sr (last access: 6 December 2024), 2023.
UNEP: GEO-6 Regional Assessment for Asia and the Pacific. United Nations Environment Programme, Nairobi, Kenya, Online: http://www.unep.org/resources/report/geo-6-global-environment-outlook-regional-assessment-asia-and-pacific (last access: 20 May 2024), 2016.
UNEP: World Environment Situation Room and WESR CCA: Executive Briefing – November 2021, https://wedocs.unep.org/handle/20.500.11822/39684 (last access: 20 May 2024), 2021.
UNEP: World Environment Situation Room (WESR), Online: https://wesr.unepgrid.ch (last access: 20 May 2024), 2023.
UNISDR: UNISDR Terminology on Disaster Risk Reduction. Geneva, Switzerland, United Nations Office for Disaster Risk Reduction (UNDRR), https://www.undrr.org/publication/2009-unisdr-terminology-disaster-risk-reduction (last access: 20 May 2024), 2009.
UNISDR: Sendai framework for disaster risk reduction 2015–2030, Geneva, UNISDR, https://www.undrr.org/media/16176/download?startDownload=20241206 (last access: 6 December 2024), 2015a.
UNISDR: Reflection paper: Disaster risk reduction and resilience in the 2030 Agenda for Sustainable Development, New York, UNHQ Liaison Office, 2015b.
United Nations: Paris Agreement, United Nations, New York, https://unfccc.int/process-and-meetings/the-paris-agreement (last access: 6 December 2024), 2015a.
United Nations: Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1, 25 September 2015), United Nations, New York, NY, https://sdgs.un.org/2030agenda (last access: 6 December 2024), 2015b.
United Nations: The New Urban Agenda. United Nations, New York, 29 pp., https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_71_256.pdf (last access: 6 December 2024), 2017.
van de Lindt, J. W., Kruse, J., Cox, D. T., Gardoni, P., Lee, J. S., Padgett, J., McAllister, T. P., Barbosa, A., Cutler, H., Van Zandt, S., Rosenheim, N., Navarro, C. M., Sutley, E., and Hamideh, S.: The interdependent networked community resilience modeling environment (IN-CORE), Resilient Cities and Structures, 2, 57–66, https://doi.org/10.1016/j.rcns.2023.07.004, 2023.
van Westen, C. J., Kappes, M. S., Luna, B. Q., Frigerio, S., Glade, T., and Malet, J.-P.: Medium-Scale Multi-hazard Risk Assessment of Gravitational Processes, in: Mountain Risks: From Prediction to Management and Governance, vol. 34, edited by: Van Asch, T., Corominas, J., Greiving, S., Malet, J.-P., and Sterlacchini, S., Springer Netherlands, Dordrecht, 201–231, https://doi.org/10.1007/978-94-007-6769-0_7, 2014a.
van Westen, C. J., Bakker, W. H., Andrejchenko, V., Zhang, K., Berlin, J., Cristal, I., and Olyazadeh, R.: RiskChanges: a Spatial Decision Support System for analysing changing hydro-meteorological risk, In: International Conference “Analysis and Management of Changing Risks for Natural Hazards”, Padua, Italy, 18–19 November 2014, http://www.changes-itn.eu/Portals/0/Content/2014/Final%20conference/abstracts/EO5_Abstract_vanWesten_et_al.pdf (last access: 6 December 2024), 2014b.
van Westen, C., Hazarika, M., Dahal, A., Kshetri, T., Shakya, A., and Nashrrullah, S.: The RiskChanges tool for multi-hazard risk-informed planning at local government level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3026, https://doi.org/10.5194/egusphere-egu22-3026, 2022.
Villar-Vega, M., Silva, V., Crowley, H., Yepes, C., Tarque, N., Acevedo, A. B., Hube, M. A., Gustavo, C. D., and María, H. S.: 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.
Wald, D. and Lin, K.-W.: USGS ShakeCast, USGS ShakeCast, Geological Survey (U. S.), 2007-3086, https://doi.org/10.3133/fs20073086, 2007.
Wald, D. J., Jaiswal, K. S., Marano, K. D., Bausch, D. B., and Hearne, M. G., 2010, PAGER—Rapid assessment of an earthquake's impact: U. S. Geological Survey Fact Sheet 2010–3036, 4 pp., https://pubs.usgs.gov/fs/2010/3036/pdf/FS10-3036.pdf (last access: 12 December 2024), 2011.
Ward, P. J., Blauhut, V., Bloemendaal, N., Daniell, J. E., de Ruiter, M. C., Duncan, M. J., Emberson, R., Jenkins, S. F., Kirschbaum, D., Kunz, M., Mohr, S., Muis, S., Riddell, G. A., Schäfer, A., Stanley, T., Veldkamp, T. I. E., and Winsemius, H. C.: Review article: Natural hazard risk assessments at the global scale, Nat. Hazards Earth Syst. Sci., 20, 1069–1096, https://doi.org/10.5194/nhess-20-1069-2020, 2020.
Weatherill, G., Pittore, M., Haas, M., Brinckmann, N., Rüster, M., and Gomez-Zapata, J. C.: Shakyground: a web service to serve GMPE-based ground motion fields. V. 1.0, GFZ Data Services [data set], https://doi.org/10.5880/riesgos.2021.004, 2021.
Weatherill, G., Crowley, H., Roullé, A., Tourlière, B., Lemoine, A., Gracianne, C., Kotha, S. R., and Cotton, F.: Modelling site response at regional scale for the 2020 European Seismic Risk Model (ESRM20), B. Earthq. Eng., 21, 665–714, https://doi.org/10.1007/s10518-022-01526-5, 2023.
Wieland, M., Pittore, M., Parolai, S., Zschau, J., Moldobekov, B., and Begaliev, U.: Estimating building inventory for rapid seismic vulnerability assessment: Towards an integrated approach based on multi-source imaging, Soil Dyn. Earthq. Eng., 36, 70–83, https://doi.org/10.1016/j.soildyn.2012.01.003, 2012.
WMO: Weather-related disasters increase over past 50 years, causing more damage but fewer deaths, Online: https://www.unwater.org/news/weather-related-disasters-more-damage-fewer-deaths (last access: 20 May 2024), 2021.
World Bank Group: The World Bank Open Knowledge Repository (OKR), https://openknowledge.worldbank.org/ (last access: 6 December 2024), 2021.
WPS: OGC Web Processing Service 2.0 Interface Standard, Revised Date: 16 February 2018, http://docs.opengeospatial.org/is/14-065/14-065.html (last access: 20 May 2024), 2018.
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.
Zschau, J.: Where are we with multihazards, multirisks assessment capacities?, in: Science for disaster risk management 2017: knowing better and losing less, edited by: Poljansek, K., Marin Ferrer, M., De Groeve, T., and Clark, I., European Union, 98–115, https://doi.org/10.2788/688605, 2017.
Short summary
In this paper, we provide a brief introduction of the paradigm shift from managing disasters to managing risks, followed by single-hazard to multi-risk assessment. We highlight four global strategies that address disaster risk reduction and call for action. Subsequently, we present a conceptual approach for multi-risk assessment which was designed to serve potential users like disaster risk managers, urban planners or operators of critical infrastructure to increase their capabilities.
In this paper, we provide a brief introduction of the paradigm shift from managing disasters to...
Special issue
Altmetrics
Final-revised paper
Preprint