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Natural hazard uncertainty assessmen...

Riley, Karin.

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Natural hazard uncertainty assessment = modeling and decision support /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Natural hazard uncertainty assessment/ Karin Riley, Peter Webley, Matthew Thompson, editors.
其他題名:	modeling and decision support /
其他作者:	Riley, Karin.
出版者:	Washington, D.C. :American Geophysical Union ; : c2017.,
面頁冊數:	1 online resource.
附註:	Includes index.
標題:	Natural disasters - Mathematical models. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119028116
ISBN:	9781119028116

Natural hazard uncertainty assessment = modeling and decision support /
Natural hazard uncertainty assessmentmodeling and decision support /[electronic resource] :Karin Riley, Peter Webley, Matthew Thompson, editors. - 1st ed. - Washington, D.C. :American Geophysical Union ;c2017. - 1 online resource. - Geophysical monograph series ;223. - Geophysical monograph ;223..

Includes index.

Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts. In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment. We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope. Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government. Key themes and highlights include: -Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included -Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty -Emphasis on the impacts of climate change on natural hazard processes and outcomes -Recommendations for cross-disciplinary and science transfer across natural hazard sciences This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists.

ISBN: 9781119028116Subjects--Topical Terms:

874465
Natural disasters
--Mathematical models.

LC Class. No.: GB5014

Dewey Class. No.: 363.34

Natural hazard uncertainty assessment = modeling and decision support /
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520 $a Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts. In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment. We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope. Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government. Key themes and highlights include: -Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included -Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty -Emphasis on the impacts of climate change on natural hazard processes and outcomes -Recommendations for cross-disciplinary and science transfer across natural hazard sciences This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists.
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