IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v84y2016i2d10.1007_s11069-016-2487-6.html
   My bibliography  Save this article

Building climate change into risk assessments

Author

Listed:
  • Alex Coletti

    (SM Resources Corporation)

  • Antonio De Nicola

    (ENEA)

  • Maria Luisa Villani

    (ENEA)

Abstract

Community managers and planners have an increasing need for assessing system failure risks as they relate to fact-based information on weather extremes and climate change. We illustrate a model that defines the services a software system can provide to facilitate the discovery of useful information by stakeholders with different technical background wanting to reach a fact-based consensus on risks, hazards, and vulnerabilities. Decision support systems succeed in facilitating the analysis of past severe weather events but provide limited support for the analysis of hazards related to climate change. Severe weather data enable estimates of the ability of an exposed system to withstand environmental extreme values, but the estimates of their impact on communities remain largely undetermined and prone to divergent interpretations. This study proposes a model that is built on the experience of a decision support system (DSS) that is dedicated to guide users through a stakeholder-based vulnerability assessment of community water systems. The DSS integrated data sources into an online environment so that perceived risks—defined and prioritized qualitatively by users—could be compared and discussed against the impacts that past events have had on the community. To make DSS useful for practical decision making related to the complex issues, such as those encountered in the case of climate change, we propose a model with a prototype design suitable for semantic web applications where the various entities are connected by an ontology that defines relative concepts and relationships.

Suggested Citation

  • Alex Coletti & Antonio De Nicola & Maria Luisa Villani, 2016. "Building climate change into risk assessments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 1307-1325, November.
    • Handle: RePEc:spr:nathaz:v:84:y:2016:i:2:d:10.1007_s11069-016-2487-6
    DOI: 10.1007/s11069-016-2487-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2487-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-016-2487-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alessandro Pagano & Raffaele Giordano & Ivan Portoghese & Umberto Fratino & Michele Vurro, 2014. "A Bayesian vulnerability assessment tool for drinking water mains under extreme events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 2193-2227, December.
    2. Bing Wang & Su-Yan Pan & Ruo-Yu Ke & Ke Wang & Yi-Ming Wei, 2014. "An overview of climate change vulnerability: a bibliometric analysis based on Web of Science database," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1649-1666, December.
    3. Angelika Wirtz & Wolfgang Kron & Petra Löw & Markus Steuer, 2014. "The need for data: natural disasters and the challenges of database management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(1), pages 135-157, January.
    4. Christian Bizer & Tom Heath & Tim Berners-Lee, 2009. "Linked Data - The Story So Far," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 5(3), pages 1-22, July.
    5. Scott Miles, 2011. "Participatory model assessment of earthquake-induced landslide hazard models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 56(3), pages 749-766, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonio De Nicola & Maria Luisa Villani, 2021. "Smart City Ontologies and Their Applications: A Systematic Literature Review," Sustainability, MDPI, vol. 13(10), pages 1-40, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mieko Kumasaki & Malcolm King & Mitsuru Arai & Lili Yang, 2016. "Anatomy of cascading natural disasters in Japan: main modes and linkages," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1425-1441, February.
    2. Mieko Kumasaki & Malcolm King & Mitsuru Arai & Lili Yang, 2016. "Anatomy of cascading natural disasters in Japan: main modes and linkages," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1425-1441, February.
    3. Gallego-Losada, María-Jesús & Montero-Navarro, Antonio & García-Abajo, Elisa & Gallego-Losada, Rocío, 2023. "Digital financial inclusion. Visualizing the academic literature," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Anne E Thessen & Cynthia Sims Parr, 2014. "Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    5. Kurt Sandkuhl & Hans-Georg Fill & Stijn Hoppenbrouwers & John Krogstie & Florian Matthes & Andreas Opdahl & Gerhard Schwabe & Ömer Uludag & Robert Winter, 2018. "From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(1), pages 69-80, February.
    6. Phillip Lord & Simon Cockell & Robert Stevens, 2012. "Three Steps to Heaven: Semantic Publishing in a Real World Workflow," Future Internet, MDPI, vol. 4(4), pages 1-12, November.
    7. Almat Kabyl & Ming Yang & Dhawal Shah & Arshad Ahmad, 2022. "Bibliometric Analysis of Accidental Oil Spills in Ice-Infested Waters," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    8. Wuhui Chen & Incheon Paik, 2013. "Improving efficiency of service discovery using Linked data-based service publication," Information Systems Frontiers, Springer, vol. 15(4), pages 613-625, September.
    9. Aleixandre-Benavent, Rafael & Aleixandre-Tudó, José Luis & Castelló-Cogollos, Lourdes & Aleixandre, José Luis, 2018. "Trends in global research in deforestation. A bibliometric analysis," Land Use Policy, Elsevier, vol. 72(C), pages 293-302.
    10. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.
    11. Bing Wang & Hua-Nan Li & Xiao-Chen Yuan & Zhen-Ming Sun, 2017. "Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model," Energies, MDPI, vol. 10(12), pages 1-14, November.
    12. Xavier Romão & Esmeralda Paupério, 2016. "A framework to assess quality and uncertainty in disaster loss data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(2), pages 1077-1102, September.
    13. Xiang Zheng & Chuyao Feng & Mikio Ishiwatari, 2022. "Examining the Indirect Death Surveillance System of The Great East Japan Earthquake and Tsunami," IJERPH, MDPI, vol. 19(19), pages 1-15, September.
    14. Long Ho & Peter Goethals, 2020. "Research hotspots and current challenges of lakes and reservoirs: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 603-631, July.
    15. Gabriela Reis & Francisco Assis Souza Filho & Donald Robert Nelson & Renan Vieira Rocha & Samiria Maria Oliveira Silva, 2020. "Development of a drought vulnerability index using MCDM and GIS: study case in São Paulo and Ceará, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1781-1799, November.
    16. Petra Tschakert & Jon Barnett & Neville Ellis & Carmen Lawrence & Nancy Tuana & Mark New & Carmen Elrick‐Barr & Ram Pandit & David Pannell, 2017. "Climate change and loss, as if people mattered: values, places, and experiences," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 8(5), September.
    17. Wang, Bing & Ke, Ruo-Yu & Yuan, Xiao-Chen & Wei, Yi-Ming, 2014. "China׳s regional assessment of renewable energy vulnerability to climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 185-195.
    18. Cao, Quoc Dung & Miles, Scott B. & Choe, Youngjun, 2022. "Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. E. G. Stephan & T. O. Elsethagen & L. K. Berg & M. C. Macduff & P. R. Paulson & W. J. Shaw & C. Sivaraman & W. P. Smith & A. Wynne, 2016. "Semantic catalog of things, services, and data to support a wind data management facility," Information Systems Frontiers, Springer, vol. 18(4), pages 679-691, August.
    20. Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:84:y:2016:i:2:d:10.1007_s11069-016-2487-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.