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Mario Brito, Gwyn Griffiths, James Ferguson, David Hopkin, Richard Mills, Richard Pederson, and Erin MacNeil

risk management is informed by assessments provided by a group of experts, where the final assessment is one that represents the group judgment. Individual expert judgments can be aggregated mathematically or behaviorally to reach this group judgment. Previously, expert judgments have been mathematically aggregated using the linear opinion pool, where experts have been kept separate during the elicitation ( Clemen and Winkler 1999 ; Griffiths et al. 2009 ; Brito et al. 2010 ). There are different

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Julie L. Demuth, Rebecca E. Morss, Leysia Palen, Kenneth M. Anderson, Jennings Anderson, Marina Kogan, Kevin Stowe, Melissa Bica, Heather Lazrus, Olga Wilhelmi, and Jen Henderson

1. Introduction The risks posed by many natural hazards are dynamic in that the threat and information available about it evolve. When a hurricane threatens a coastline, for example, its position and intensity changes, and forecast and preparedness information is refined as the storm approaches. People’s assessments of and responses to natural hazard risks are also dynamic, as individuals process information and interact with each other to communicate about, interpret, and respond to the

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John A. Hart and Ariel E. Cohen

Risk Assessment Model (SSCRAM). The comparisons of meteorological parameters (e.g., measures of buoyancy and vertical shear) to past severe weather occurrences provide a background for anticipating severe storm risk based on initial environmental information (e.g., Rasmussen and Blanchard 1998 ; Thompson et al. 2003 , 2007 ; Craven and Brooks 2004 ). These studies provide background for forecasters to anticipate future severe weather occurrences based on past ones using meteorological parameter

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Timothy M. Hall and Stephen Jewson

system that allows direct comparison between the models. We find that bias in the track model is more than compensated for on most regional-scale coast segments by the reduction of sampling error compared to the local model. This is the first time, to our knowledge, that the use of basinwide statistical track models has been rigorously justified for use in TC landfall risk assessment. 2. Local model The local model makes predictions of future TC landfall rates on a segment of coastline using only

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Stéphane Hallegatte

–237 . Emanuel , K. , C. DesAutels , C. Holloway , and R. Korty , 2004 : Environmental control of tropical cyclone intensity. J. Atmos. Sci. , 61 , 843 – 858 . Emanuel , K. , S. Ravela , E. Vivant , and C. Risi , 2006 : A statistical deterministic approach to hurricane risk assessment. Bull. Amer. Meteor. Soc. , 87 , 299 – 314 . Fankhauser , S. , 1995 : Valuing Climate Change: The Economics of the Greenhouse . Earthscan Publications, 180 pp . Hallegatte , S. , 2006 : A

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Paul A. T. Higgins and Jonah V. Steinbuck

1. Introduction Understanding the potential consequences of climate change to society is extremely challenging because climate impacts will depend on a multitude of contributing factors that interact in complicated ways and that are characterized by varying degrees of uncertainty ( Moss 2011 ). For example, the risk assessment process must synthesize information from numerous disciplines that span the physical sciences (e.g., how much and how fast climate changes), natural sciences (e.g., how

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Christopher A. Roseman and Brian M. Argrow

the safety management system manual ( Bristol 2019 ). The manual describes the five steps of the “DIAAT” process: describing the system, identifying hazards, then analyzing, assessing, and treating the risk. Describing the system involves outlining the scope of the risk analysis being performed. After the system is described, hazards within the risk assessment scope must be identified. A hazard is a condition that could foreseeably cause or contribute to an accident. Analyzing hazard risk is

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Rowan T. Sutton

climate assessments, especially those of the Intergovernmental Panel on Climate Change (IPCC)? From the perspective of societal needs—that is, the needs of decision-makers in governments, businesses, or civil society—climate change is a problem in risk assessment 1 and risk management. Therefore, a central question is, What information can science provide to meet these needs? In this article, I want to focus particularly on the contribution of physical climate science, and the community of scientists

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Duzgun Agdas, Forrest J. Masters, and Gregory D. Webster

experience. Potential hazards of shadow evacuation behavior in no-evacuation zones are also discussed in detail by Dueñas-Osorio et al. (2012) . While much of the literature focuses on the potential benefits of better risk perception and increased impact of better risk assessment in improving mitigation and preparedness activities, the authors discuss the potential issues associated with shadow evacuations. The authors also discuss the discrepancies in storm surge, which is the more destructive

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D. W. Wanik, E. N. Anagnostou, M. Astitha, B. M. Hartman, G. M. Lackmann, J. Yang, D. Cerrai, J. He, and M. E. B. Frediani

findings and future research directions. 2. Weather data a. Background Within the IPCC Fourth Assessment Report (AR4) one can find several future emissions scenarios and the associated impact on global average temperature and sea level rise; these scenarios include keeping emissions at constant levels from the year 2000 and subsequent scenarios with increased emissions. In our study, we utilized the A2 emissions scenario, which describes a heterogeneous world with increasing population and carbon

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