Losses from natural hazards, including geophysical and hydrometeorological hazards, have been increasing worldwide. This review focuses on the process by which scientific evidence about natural hazards is applied to support decision making. Decision analysis typically involves estimating the probability of extreme events; assessing the potential impacts of those events from a variety of perspectives; and evaluating options to plan for, mitigate, or react to events. We consider issues that affect decisions made across a range of natural hazards, summarize decision methodologies, and provide examples of applications of decision analysis to the management of natural hazards. We conclude that there is potential for further exchange of ideas and experience between natural hazard research communities on decision analysis approaches. Broader application of decision methodologies to natural hazard management and evaluation of existing decision approaches can potentially lead to more efficient allocation of scarce resources and more efficient risk management.


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Literature Cited

  1. 1. Munich Reinsurance America, Inc. (Munich Re) 2015. Natural catastrophes 2014: analyses, assessments, positions. TOPICS Geo 2014:48–49 [Google Scholar]
  2. 2. United Nations Off. Disaster Risk Reduct. (UNISDR) 2015. Sendai Framework for Disaster Risk Reduction 2015–2030 Geneva, Switz.: UN
  3. Kellett J, Caravani A. 3.  2013. Financing Disaster Risk Reduction: A 20 Year Story for International Aid London/Washington, DC: Overseas Dev. Inst., Glob. Facil. Disaster Reduct. Recovery
  4. Kleindorfer PR, Kunreuther HG, Schoemaker PJH. 4.  1993. Decision Sciences: An Integrative Perspective Cambridge, UK: Cambridge Univ. Press
  5. Kahneman D, Tversky A. 5.  1979. Prospect theory: an analysis of decision under risk. Econometrica 47:2263–92 [Google Scholar]
  6. Greenberg M, Haas C, Cox A Jr., Lowrie K, McComas K, North W. 6.  2012. Ten most important accomplishments in risk analysis, 1980–2010. Risk Anal 32:771–81 [Google Scholar]
  7. Wilson RS, Winter PL, Maguire LA, Ascher T. 7.  2011. Managing wildfire events: risk-based decision making among a group of federal fire managers. Risk Anal 31:5805–18 [Google Scholar]
  8. Kahneman D.8.  2011. Thinking, Fast and Slow New York: Farrar, Straus, Giroux
  9. Keefer DL, Kirkwood CW, Corner JL. 9.  2004. Perspective on decision analysis applications, 1990–2001. Decis. Anal. 1:14–22 [Google Scholar]
  10. Ackoff RL.10.  1974. Redesigning the Future: A Systems Approach to Societal Problems New York: Wiley
  11. Rittel HJ, Webber M. 11.  1973. Dilemmas in a general theory of planning. Policy Sci 4:2155–69 [Google Scholar]
  12. Funtowicz SO, Ravetz JR. 12.  1990. Uncertainty and Quality in Science for Policy Berlin: Kluwer
  13. Frame B.13.  2008. “Wicked,” “messy,” and “clumsy”: long-term frameworks for sustainability. Environ. Plann. C 26:61113–28 [Google Scholar]
  14. Armstrong JS.14.  2001. Selecting forecasting methods. Principles of Forecasting: A Handbook for Researchers and Practitioners JS Armstrong 365–86 Boston: Springer [Google Scholar]
  15. Green K.15.  2002. Forecasting decisions in conflict situations: a comparison of game theory, role-playing, and unaided judgement. Int. J. Forecast. 18:321–44 [Google Scholar]
  16. Jones R, Patwardhan A, Cohen S, Dessai S, Lammel A. 16.  et al. 2014. Foundations for decision making. See Ref. 84 195–228
  17. Hammond JS, Keeney RL, Raiffa H. 17.  1998. Smart Choices: A Practical Guide to Making Better Decisions Boston: Harvard Bus. Sch.
  18. Morgan MG, Henrion M, Small M. 18.  1990. Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis Cambridge, UK: Cambridge Univ. Press
  19. Ranger N, Millner A, Dietz S, Frankhauser S, Ruta G. 19.  et al. 2010. Adaptation in the UK: A Decision-Making Process London: Grantham Inst. Clim. Chang.
  20. 20. Government Office for Science 2011. Code of Practice for Scientific Advisory Committees London: Dep. Bus., Innov. Skills
  21. Harris GP.21.  2007. Seeking Sustainability in an Age of Complexity Cambridge, UK: Cambridge Univ. Press
  22. French S, Maule J, Papamichail N. 22.  2009. Decision Behaviour, Analysis and Support Cambridge, UK: Cambridge Univ. Press
  23. Gigerenzer G, Todd P. 23.  1999. Simple Heuristics that Make Us Smart New York: Oxford Univ. Press
  24. Klein G, Calderwood R, Clinton-Cirocco A. 24.  2010. Rapid decision making on the fire ground: the original study plus a postscript. J. Cogn. Eng. Decis. Making 4:3186–209 [Google Scholar]
  25. Nalebuff B, Brandenburger A. 25.  1996. Co-opetition London: HarperCollins Bus.
  26. Madani K.26.  2010. Game theory and water resources. J. Hydrol. 381:3–4225–38 [Google Scholar]
  27. Thaler R.27.  1980. Toward a positive theory of consumer choice. J. Econ. Behav. Organ. 1:139–60 [Google Scholar]
  28. Grove WM, Zald DH, Lebow BS, Snitz BE, Nelson C. 28.  2000. Clinical versus mechanical prediction: a meta-analysis. Psychol. Assess. 12:119–30 [Google Scholar]
  29. 29. World Economic Forum 2011. A Vision for Managing Natural Disaster Risk: Proposals for Public/Private Stakeholder Solutions Geneva, Switz: World Econ. Forum
  30. Rougier J, Sparks S, Hill LJ. 30.  2013. Risk and Uncertainty Assessment for Natural Hazards Cambridge, UK: Cambridge Univ. Press
  31. Tacnet JM, Batton-Hubert M, Jean-Dezert, Richard D. 31.  2012. Decision support tools for natural hazard management under uncertainty. Presented at 12th Congress Interpraevent. Grenoble, France
  32. Tall A, Mason SJ, van Aalst M, Suarez P, Ait-Chellouche Y. 32.  et al. 2012. Using seasonal climate forecasts to guide disaster management: the Red Cross experience during the 2008 West Africa floods. Int. J. Geophys. 2012:986016 [Google Scholar]
  33. 33. World Health Organ 2011. Useful definitions and early warning information for natural hazards. Disaster Risk Management for Health Fact Sheets. Geneva: World Health Organ http://www.who.int/hac/events/drm_fact_sheet_early_warning_natural_hazards.pdf [Google Scholar]
  34. Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T. 34.  et al. 2013. Long-term climate change: projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change T Stocker, D Qin, G Plattner, M Tignor, S Allen 1029–136 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  35. Fischer EM, Knutti R. 35.  2015. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5:6560–64 [Google Scholar]
  36. Vecchia AV.36.  2001. A unified approach to probabilistic risk assessments for earthquakes, floods, landslides and volcanoes. Proc. Multidisciplinary Workshop, Golden, CO, Nov. 16–17, 1999 Open File Rep. 2001-324. US Geol. Surv. Urban Hazards Initiative, Golden, CO
  37. Maier HR, Guillaume JHA, van Delden H, Riddel GA, Haasnoot M, Kwakkel JH. 37.  2016. An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?. Environ. Model. Softw. 81:154–64 [Google Scholar]
  38. Løvholt F, Setiati NJ, Birkmann J, Harbitz CB, Fernando N. 38.  et al. 2014. Tsunami risk reduction—are we better prepared today than in 2004?. Int. J. Disaster Risk Reduct. 10:Part A127–42 [Google Scholar]
  39. Haraguchi M, Lall U. 39.  2015. Flood risks and impacts: a case study of Thailand's floods in 2011 and research questions for supply chain decision making. Int. J. Disaster Risk Reduct. 14:Part 3256–72 [Google Scholar]
  40. 40. Int. Displac. Monit. Cent. (IDMC) 2015. Global Estimates 2015: People Displaced by Disasters Geneva, Switz.: Norw. Refug. Counc.
  41. Hall J, Anderson M. 41.  2002. Handling uncertainty in extreme or unrepeatable hydrological processes—the need for an alternative paradigm. Hydrol. Process. 16:91867–70 [Google Scholar]
  42. Blazkova S, Beven K. 42.  2009. A limits of acceptability approach to model evaluation and uncertainty estimation in flood frequency estimation by continuous simulation: Skalka catchment, Czech Republic. Water Resour. Res. 45:12W00B16 [Google Scholar]
  43. Westerberg I, Guerrero J-L, Seibert J, Beven KJ, Halldin S. 43.  2011. Stage-discharge uncertainty derived with a non-stationary rating curve in the Choluteca River, Honduras. Hydrol. Process. 25:4603–13 [Google Scholar]
  44. McMillan H, Krueger T, Freer J. 44.  2012. Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality. Hydrol. Process. 26:264078–111 [Google Scholar]
  45. Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P. 45.  et al. 2014. Recommendations for the quantitative analysis of landslide risk. Bull. Eng. Geol. Environ. 73:2209–63 [Google Scholar]
  46. Ibsen M-L, Brunsden D. 46.  1996. The nature, use and problems of historical archives for the temporal occurrence of landslides, with specific reference to the south coast of Britain, Ventnor, Isle of Wight. Geomorphology 15:3–4241–58 [Google Scholar]
  47. Lumb P.47.  1975. Slope failures in Hong Kong. Q. J. Eng. Geol. Hydrogeol. 8:131–65 [Google Scholar]
  48. David N, Alpert P, Messer H. 48.  2009. Technical note: novel method for water vapour monitoring using wireless communication networks measurements. Atmos. Chem. Phys. 9:72413–18 [Google Scholar]
  49. Beven KJ.49.  2009. Environmental Modelling: An Uncertain Future? An Introduction to Techniques for Uncertainty Estimation in Environmental Prediction London: Routledge
  50. Mackie S, Watson M. 50.  2014. Probabilistic detection of volcanic ash using a Bayesian approach. J. Geophys. Res.: Atmospheres 119:52409–28 [Google Scholar]
  51. Allen M.51.  2003. Liability for climate change. Nature 421:6926891–92 [Google Scholar]
  52. Dessai S, Hulme M, Lempert R, Pielke R. 52.  2009. Climate prediction: a limit to adaptation. Adapting to Climate Change: Thresholds, Values, Governance WN Adger, I Lorenzoni, KL O'Brien 64–78 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  53. Brown C, Werick W, Leger W, Fay D. 53.  2011. A decision-analytic approach to managing climate risks: application to the Upper Great Lakes. J. Am. Water Resour. Assoc. 47:3524–34 [Google Scholar]
  54. Kappes MS, Keiler M, von Elverfeldt K, Glade T. 54.  2012. Challenges of analyzing multi-hazard risk: a review. Nat. Hazards 64:21925–58 [Google Scholar]
  55. Parry GW.55.  1996. The characterization of uncertainty in probabilistic risk assessments of complex systems. Reliab. Eng. Syst. Saf. 54:2–3119–26 [Google Scholar]
  56. Ferson S, Ginzburg LR. 56.  1996. Different methods are needed to propagate ignorance and variability. Reliab. Eng. Syst. Saf. 54:2–3133–44 [Google Scholar]
  57. Winkler RL.57.  1996. Uncertainty in probabilistic risk assessment. Reliab. Eng. Syst. Saf. 54:2–3127–32 [Google Scholar]
  58. Hall J.58.  2013. Flood risk management: decision making under uncertainty. Uncertainty in Flood Risk Management Beven K, Hall J. 3–24 London: Imperial Coll. Press [Google Scholar]
  59. Rosner A, Vogel RM, Kirshen PH. 59.  2014. A risk-based approach to flood management decisions in a nonstationary world. Water Resour. Res. 50:31928–42 [Google Scholar]
  60. Field CB, Barros V, Stocker TF, Qin D, Dokken DJ. 60.  et al. 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change Cambridge, UK: Cambridge Univ. Press
  61. Parker R, Little K, Hauser S. 61.  2007. Development actions and the rising incidence of disasters Rep. 53369, Indep. Eval Group, World Bank Washington, DC:
  62. Lempert R, Kalra N. 62.  2014. Managing climate risks in developing countries with robust decision making Rep., World Resour., Washington, DC
  63. Penning-Rowsell EC, Priest S, Parker D, Morris J, Tunstall S. 63.  et al. 2013. Flood and Coastal Erosion Risk Management: A Manual for Economic Appraisal Abingdon, UK: Routledge
  64. Vogel RM, Yaindl C, Walter M. 64.  2011. Nonstationarity: flood magnification and recurrence reduction factors in the United States. J. Am. Water Resour. Assoc. 47:3464–74 [Google Scholar]
  65. Prudhomme C, Wilby RL, Crooks S, Kay AL, Reynard NS. 65.  2010. Scenario-neutral approach to climate change impact studies: application to flood risk. J. Hydrol. 390:3–4198–209 [Google Scholar]
  66. Brown C, Ghile Y, Laverty M, Li K. 66.  2012. Decision scaling: linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resour. Res. 48:9W09537 [Google Scholar]
  67. Aceves-Quesada JF, Díaz-Salgado J, López-Blanco J. 67.  2007. Vulnerability assessment in a volcanic risk evaluation in Central Mexico through a multi-criteria-GIS approach. Nat. Hazards 40:2339–56 [Google Scholar]
  68. Walker BB, Taylor-Noonan C, Tabbernor A, McKinnon T, Bal H. 68.  et al. 2014. A multi-criteria evaluation model of earthquake vulnerability in Victoria, British Columbia. Nat. Hazards 74:21209–22 [Google Scholar]
  69. Feizizadeh B, Blaschke T. 69.  2013. GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat. Hazards 65:32105–28 [Google Scholar]
  70. Treasury HM. 70.  2011. The Green Book: Appraisal and Evaluation in Central Government. London: TSO
  71. 71. Environ. Agency, Defra, Welsh Gov., Nat. Resour. Wales, Ofwat 2012. Water Resources Planning Guideline Bristol, UK: Environ. Agency
  72. Bommier A, Villneuve B. 72.  2012. Risk aversion and the value of risk to life. J. Risk Insur. 79:177–104 [Google Scholar]
  73. Triantaphyllou E.73.  2013. Multi-Criteria Decision Making Methods: A Comparative Study Berlin: Springer
  74. Podvezko V, Podviezko A. 74.  2010. Dependence of multi-criteria evaluation result on choice of preference functions and their parameters. Ukio Technologinis Ekonominis Vystymas 16:1143–58 [Google Scholar]
  75. Keeney RL, Raiffa H. 75.  1993. Decisions with Multiple Objectives—Preferences and Value Tradeoffs Cambridge, UK: Cambridge Univ. Press
  76. Tanner T, Lewis D, Wrathall D, Bronen R, Cradock-Henry. 76.  et al. 2015. Livelihood resilience in the face of climate change. Nat. Clim. Change 5:123–26 [Google Scholar]
  77. Ozdemir MS, Saaty TL. 77.  2006. The unknown in decision making: what to do about it. Eur. J. Oper. Res. 174:1349–59 [Google Scholar]
  78. Morrow B.78.  2009. Risk Behavior and Risk Communication: Synthesis and Expert Interviews Miami, FL: Soc. Res.
  79. Gamper CD.79.  2008. The political economy of public participation in natural hazard decisions—a theoretical review and an exemplary case of the decision framework of Austrian hazard zone mapping. Nat. Hazards Earth Syst. Sci. 8:2233–41 [Google Scholar]
  80. Berkes F.80.  2007. Understanding uncertainty and reducing vulnerability: lessons from resilience thinking. Nat. Hazards 41:2283–95 [Google Scholar]
  81. Arrow KJ.81.  1971. Social Choice and Individual Values. New Haven, CT: Yale Univ. Press
  82. Zhang F, Ignatius J, Zhao Y, Lim CP, Ghasemi M, Ng PS. 82.  2015. An improved consensus-based group decision making model with heterogeneous information. Appl. Soft Comput. 35:850–63 [Google Scholar]
  83. Lee G, Jun KS, Chung ES. 83.  2015. Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method. Nat. Hazards Earth Syst. Sci. 15:4863–74 [Google Scholar]
  84. CB Field, VR Barros, DJ Dokken, KJ Mach, MD Mastrandrea. 84.  et al. 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge Univ. Press
  85. Mejía-Navarro M, Garcia LA. 85.  1996. Natural hazard and risk assessment using decision support systems, application: Glenwood Springs, Colorado. Environ. Eng. Geosci. II:3299–324 [Google Scholar]
  86. Madani K, Lund JR. 86.  2011. A Monte-Carlo game theoretic approach for Multi-Criteria Decision Making under uncertainty. Adv. Water Resour. 34:607–16 [Google Scholar]
  87. 87. National Research Council 1988. Probabilistic Seismic Hazard Analysis Washington, DC: Nat. Acad. Press
  88. 88. Nat. Environ. Res. Counc 1975. Flood Studies Report. Swindon, UK: NERC
  89. Vrijling JK, Schweckendiek T, Kanning W. 89.  2011. Safety standards of flood defenses. Proc. 3rd Int. Symp. Geotech, Safety and Risk. June 2–3, Munich. http://repository.tudelft.nl/islandora/object/uuid:652da5f1-1b65-4619-91f0-20cbf669d532/datastream/OBJ/download
  90. Hall JW, Watts G, Keil M, de Vial L, Street R. 90.  et al. 2012. Towards risk-based water resources planning in England and Wales under a changing climate. Water Environ. J. 26:1118–29 [Google Scholar]
  91. Durage S, Wirasinghe S, Ruwanpura J. 91.  2016. Decision analysis for tornado warning and evacuation. Nat. Hazards Rev. 17:104015014 [Google Scholar]
  92. Cheng C-C, Hsu N-S, Wei C-C. 92.  2007. Decision-tree analysis on optimal release of reservoir storage under typhoon warnings. Nat. Hazards 44:165–84 [Google Scholar]
  93. Castillo-Rodríguez JT, Escuder-Bueno I, Altarejos-Garcia L, Serrano-Lombillo A. 93.  2014. The value of integrating information from multiple hazards for flood risk analysis and management. Nat. Hazards Earth Syst. Sci. 14:2379–400 [Google Scholar]
  94. Aspinall W, Woo G. 94.  2014. Santorini unrest 2011–2012: an immediate Bayesian belief network analysis of eruption scenario probabilities for urgent decision support under uncertainty. J. Appl. Volcanol. 3:12 [Google Scholar]
  95. Bayraktarli YY, Faber MH. 95.  2011. Bayesian probabilistic network approach for managing earthquake risks of cities. Georisk: Assess. Manag. Risk. Eng. Syst. Geohazards 5:12–24 [Google Scholar]
  96. Rohmer J.96.  2014. Combining meta-modeling and categorical indicators for global sensitivity analysis of long-running flow simulators with spatially dependent inputs. Comput. Geosci. 18:2171–83 [Google Scholar]
  97. Kalra NR, Groves DG, Bonzanigo L, Molina Perez E, Ramos C. 97.  et al. 2015. Robust decision-making in the water sector: a strategy for implementing Lima's long-term water resources master plan Policy Res. Work. Pap. WPS7439, World Bank
  98. Lempert R, Kalra N, Peyraud S, Mao Z, Tan SB. 98.  et al. 2013. Ensuring robust flood risk management in Ho Chi Minh City Policy Res. Work. Pap., WPS6465, World Bank
  99. Groves D, Fischbach JR, Knopman D, Johnson DR, Giglio K. 99.  et al. 2014. Strengthening Coastal Planning: How Coastal Regions Could Benefit from Louisiana's Planning and Analysis Framework Santa Monica, CA: RAND Corp.
  100. Hine D, Hall JW. 100.  2010. Information gap analysis of flood model uncertainties and regional frequency analysis. Water Resour. Res. 46:1W01514 [Google Scholar]
  101. Matrosov ES, Woods AM, Harou JJ. 101.  2013. Robust decision making and Info-Gap Decision Theory for water resource system planning. J. Hydrol. 494:43–58 [Google Scholar]
  102. Tang H, Fan D, Li D, Xue S. 102.  2015. Info-gap decision for the robust seismic design optimization of structures. J. Hunan Univ. Nat. Sci. 42:521–28 [Google Scholar]
  103. Steinschneider S, Wi S, Brown C. 103.  2015. The integrated effects of climate and hydrologic uncertainty on future flood risk assessments. Hydrol. Process 29:122823–39 [Google Scholar]
  104. Ray P, Brown C. 104.  2015. Confronting Climate Uncertainty in Water Resources Planning and Project Design: The Decision Tree Framework Washington, DC: World Bank
  105. Sayers P, Yuanyuan L, Galloway G, Penning-Rowsell E, Fuxin S. 105.  et al. 2013. Flood Risk Management: A Strategic Approach Paris: UNESCO
  106. Woodward M, Kapelan Z, Gouldby B. 106.  2014. Adaptive flood risk management under climate change uncertainty using real options and optimization. Risk Anal 34:175–92 [Google Scholar]
  107. Haasnoot M, Kwakkel JH, Walker WE, ter Maat J. 107.  2013. Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob. Environ. Change 23:2485–98 [Google Scholar]
  108. Rosenzweig C, Solecki W. 108.  2014. Hurricane Sandy and adaptation pathways in New York: lessons from a first-responder city. Glob. Environ. Change 28:0395–408 [Google Scholar]
  109. Melchers RE.109.  1999. Structural Reliability: Analysis and Prediction Chichester, UK: Wiley, 2nd ed..
  110. Murphy JM, Sexton D, Jenkins G, Boorman P, Booth B. 110.  et al. 2009. UK Climate Projections Science Report: Climate Change Projection Exeter, UK: Met Off.
  111. Sayers P, Hall J, Meadowcroft I. 111.  2002. Towards risk-based flood hazard management in the UK. Proc. ICE 150:12803)36–42 [Google Scholar]
  112. Todini E.112.  2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrol. Process. 18:142743–46 [Google Scholar]
  113. Young P, Romanowicz R, Beven K. 113.  2014. A data-based mechanistic modelling approach to real-time flood forecasting. Applied Uncertainty Analysis for Flood Risk Management K Beven, J Hall 407–61 London: Imperial Coll. Press [Google Scholar]
  114. Krzysztofowicz R.114.  1993. A theory of flood warning systems. Water Resour. Res. 29:123981–94 [Google Scholar]
  115. Dawson RJ, Hall JW, Bates PD, Nicholls RJ. 115.  2005. Quantified analysis of the probability of flooding in the Thames Estuary under imaginable worst-case sea level rise scenarios. Int. J. Water Resour. Dev. 21:4577–91 [Google Scholar]
  116. Hall JW, Evans EP, Penning-Rowsell EC, Sayers PB, Thorne CR. 116.  et al. 2003. Quantified scenarios analysis of drivers and impacts of changing flood risk in England and Wales: 2030–2100. Glob. Environ. Change Part B 5:3–451–65 [Google Scholar]
  117. Anbazhagan P, Vinod JS, Sitharam TG. 117.  2009. Probabilistic seismic hazard analysis for Bangalore. Nat. Hazards 48:2145–66 [Google Scholar]
  118. Tseng C-P, Chen C-W. 118.  2012. Natural disaster management mechanisms for probabilistic earthquake loss. Nat. Hazards 60:31055–63 [Google Scholar]
  119. Sadeghi M, Hochrainer-Stigler S, Ghafory-Ashtiany M. 119.  2015. Evaluation of earthquake mitigation measures to reduce economic and human losses: a case study to residential property owners in the metropolitan area of Shiraz, Iran. Nat. Hazards 78:31811–26 [Google Scholar]
  120. New M, Lopez A, Dessai S, Wilby R. 120.  2007. Challenges in using probabilistic climate change information for impact assessments: an example from the water sector. Philos. Trans. R. Soc. A 365:18572117–31 [Google Scholar]
  121. Borgomeo E, Hall JW, Fung F, Watts G, Colquhoun K, Lambert C. 121.  2014. Risk-based water resources planning: incorporating probabilistic nonstationary climate uncertainties. Water Resour. Res. 50:86850–73 [Google Scholar]
  122. Kull D, Mechler R, Hochrainer-Stigler S. 122.  2013. Probabilistic cost-benefit analysis of disaster risk management in a development context. Disasters 37:3374–400 [Google Scholar]
  123. Michel-Kerjan E, Hochrainer-Stigler S, Kunreuther H, Linnerooth-Bayer L, Mechler R. 123.  et al. 2013. Catastrophe risk models for evaluating disaster risk reduction investments in developing countries. Risk Anal 33:6984–99 [Google Scholar]
  124. Hochrainer-Stigler S, Mechler R, Mochizuki J. 124.  2015. A risk management tool for tackling country-wide contingent disasters: a case study on Madagascar. Environ. Model. Softw. 72:44–55 [Google Scholar]
  125. Woo G.125.  2010. Operational earthquake forecasting and risk management. Seismol. Res. Lett. 81:5778–82 [Google Scholar]
  126. Gollier C, Hammitt JK. 126.  2014. The long-run discount rate controversy. Annu. Rev. Resour. Econ. 6:1273–95 [Google Scholar]
  127. Medina-Cetina Z, Nadim F. 127.  2008. Stochastic design of an early warning system. Georisk: Assess. Manag. Risk Eng. Syst. Geohazards 2:4223–36 [Google Scholar]
  128. Hall JW, Manning LJ, Hankin RKS. 128.  2011. Bayesian calibration of a flood inundation model using spatial data. Water Resour. Res. 47:5W05529 [Google Scholar]
  129. Cooke RM.129.  1991. Experts in Uncertainty: Opinion and Subjective Probability in Science New York: Oxford Univ. Press
  130. O'Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH. 130.  et al. 2006. Uncertain Judgements: Eliciting Experts’ Probabilities Hoboken, NJ: Wiley
  131. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F. 131.  2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:1–4272–99 [Google Scholar]
  132. Hall J, Solomatine D. 132.  2008. A framework for uncertainty analysis in flood risk management decisions. Int. J. River Basin Manag. 6:285–98 [Google Scholar]
  133. Benjamin JR, Cornell CA. 133.  1970. Probability, Statistics, and Decision for Civil Engineers New York: McGraw-Hill
  134. Pearl J.134.  1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference San Mateo, CA.: Morgan Kaufmann Publ.
  135. Smith JQ.135.  2010. Bayesian Decision Analysis: Principles and Practice Cambridge, UK: Cambridge Univ. Press
  136. Pianosi F, Beven K, Freer J, Hall JW, Rougier J. 136.  et al. 2016. Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ. Model. Softw. 79:214–32 [Google Scholar]
  137. Hawkes PJ, Gouldby BP, Tawn JA, Owen MW. 137.  2002. The joint probability of waves and water levels in coastal engineering design. J. Hydraulic Res. 40:3241–51 [Google Scholar]
  138. Heffernan JE, Tawn JA. 138.  2004. A conditional approach for multivariate extreme values (with discussion). J. R. Stat. Soc.: Ser. B. 66:3497–546 [Google Scholar]
  139. Keef C, Tawn J, Svensson C. 139.  2009. Spatial risk assessment for extreme river flows. J. R. Stat. Soc.: Ser. C 58:5601–18 [Google Scholar]
  140. Beven K, Lamb R, Leedal D, Hunter N. 140.  2015. Communicating uncertainty in flood inundation mapping: a case study. Int. J. River Basin Manag. 13:3285–95 [Google Scholar]
  141. Martina MLV, Todini E, Libralon A. 141.  2006. A Bayesian decision approach to rainfall thresholds based flood warning. Hydrol. Earth Syst. Sci. 10:3413–26 [Google Scholar]
  142. Mylne KR.142.  2002. Decision-making from probability forecasts based on forecast value. Meteorological Appl 9:3307–15 [Google Scholar]
  143. Paté-Cornell ME.143.  1996. Uncertainties in risk analysis: six levels of treatment. Reliab. Eng. Syst. Saf. 54:2–395–111 [Google Scholar]
  144. Smyth A, Altay G, Deodatis G, Erdik M, Franco G. 144.  et al. 2004. Probabilistic benefit-cost analysis for earthquake damage mitigation: evaluating measures for apartment houses in Turkey. Earthquake Spectra 20:1171–203 [Google Scholar]
  145. Petersen MD, Dewey J, Hartzell S, Mueller C, Harmsen S. 145.  et al. 2004. Probabilistic seismic hazard analysis for Sumatra, Indonesia and across the Southern Malaysian Peninsula. Tectonophysics 390:1–4141–58 [Google Scholar]
  146. Bommer J, Abrahamson A. 146.  2006. Why do modern probabilistic seismic-hazard analyses often lead to increased hazard estimates?. Bull. Seismol. Soc. Am. 96:61967–77 [Google Scholar]
  147. Hill B, Aspinall W, Connor C, Komorowski J, Nakada S. 147.  2009. Recommendations for assessing volcanic hazards at sites of nuclear installations. Volcanic and Tectonic Hazard Assessment for Nuclear Facilities CB Connor, NA Chapman, LJ Connor 56692 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  148. Rayner S.148.  2007. The rise of risk and the decline of politics. Environ. Hazards 7:2165–72 [Google Scholar]
  149. Pidgeon N, Butler C. 149.  2009. Risk analysis and climate change. Environ. Polit. 18:5670–88 [Google Scholar]
  150. Hall J.150.  2007. Probabilistic climate scenarios may misrepresent uncertainty and lead to bad adaptation decisions. Hydrological Process 21:81127–29 [Google Scholar]
  151. Lempert RJ, Collins MT. 151.  2007. Managing the risk of uncertain threshold responses: comparison of robust, optimum, and precautionary approaches. Risk Anal 27:41009–26 [Google Scholar]
  152. Spiegelhalter DJ, Riesch H. 152.  2011. Don't know, can't know: embracing deeper uncertainties when analysing risks. Philos. Trans. R. Soc. A 3691956:4730–50 [Google Scholar]
  153. Lempert RJ, Popper SW, Bankes SC. 153.  2003. Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis Santa Monica, CA: RAND
  154. Kalra N, Hallegatte S, Lempert R, Brown C, Fozzard A. 154.  et al. 2014. Agreeing on robust decisions: new processes for decision making under deep uncertainty Policy Res. Work. Pap. WPS6906, World Bank
  155. Wilby RL, Dessai S. 155.  2010. Robust adaptation to climate change. Weather 65:7180–85 [Google Scholar]
  156. Huntjens P, Lebel L, Pahl-Wostl C, Camkin J, Schulze R, Kranz N. 156.  2012. Institutional design propositions for the governance of adaptation to climate change in the water sector. Glob. Environ. Change 22:167–81 [Google Scholar]
  157. Hallegatte S.157.  2009. Strategies to adapt to an uncertain climate change. Glob. Environ. Change. 19:2240–47 [Google Scholar]
  158. Stirling A.158.  2010. Keep it complex. Nature 468:73271029–31 [Google Scholar]
  159. Lindley DV.159.  1985. Making Decisions London: Wiley, 2nd ed.
  160. Ben-Haim Y.160.  2006. Info-Gap Decision Theory: Decisions Under Severe Uncertainty London: Academic, 2nd ed.
  161. Knight F.161.  1921. Risk, Uncertainty and Profit Boston: Houghton-Mifflin Co.
  162. Simon H.162.  1956. Rational choice and the structure of the environment. Psychol. Rev. 63:2129–38 [Google Scholar]
  163. Bankes SC.163.  2002. Tools and techniques for developing policies for complex and uncertain systems. Proc. Natl. Acad. Sci. 99:Suppl. 37263–66 [Google Scholar]
  164. Lempert RJ, Groves DG, Popper SW, Bankes SC. 164.  2006. A general, analytic method for generating robust strategies and narrative scenarios. Manag. Sci. 52:4514–28 [Google Scholar]
  165. Groves DG, Lempert RJ. 165.  2007. A new analytic method for finding policy-relevant scenarios. Glob. Environ. Change 17:173–85 [Google Scholar]
  166. Borgomeo E, Pflug G, Hall JW, Hochrainer-Stigler S. 166.  2015. Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit. Water Resour. Res. 51:118927–48 [Google Scholar]
  167. Lempert RJ, Popper SW, Groves DG, Kalra N, Fischbach JR. 167.  et al. 2013. Making Good Decisions Without Predictions: Robust Decision Making for Planning Under Deep Uncertainty Santa Monica, CA: RAND Corp.
  168. Bryant BP, Lempert RJ. 168.  2010. Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol. Forecast. Soc. Change 77:134–49 [Google Scholar]
  169. Lempert R, Groves D. 169.  2010. Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American West. Technol. Forecast. Soc. Change 77:6960–74 [Google Scholar]
  170. Groves DG, Fischbach JR, Bloom E, Knopman D, Keefe R. 170.  2012. Adapting to a Changing Colorado River: Making Future Water Deliveries More Reliable Through Robust Management Strategies Santa Monica, CA: RAND Corp.
  171. Fischbach JR.171.  2010. Managing New Orleans Flood Risk in an Uncertain Future Using Non-Structural Risk Mitigation Santa Monica, CA: Pardee RAND Grad. Sch.
  172. Brown A, Gawith M, Lonsdale K, Pringle P. 172.  2011. Managing Adaptation: Linking Theory and Practice Oxford, UK: UK Clim. Impacts Progr.
  173. Poff NL, Brown CM, Grantham TE, Matthews JH, Palmer MA. 173.  et al. 2015. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat. Clim. Change. doi:10.1038/nclimate2765
  174. Singh R, Wagener T, Crane R, Mann ME, Ning L. 174.  2014. A vulnerability driven approach to identify adverse climate and land use change combinations for critical hydrologic indicator thresholds: application to a watershed in Pennsylvania, USA. Water Resour. Res. 50:43409–27 [Google Scholar]
  175. Hall JW, Lempert RJ, Keller K, Hackbarth A, Mijere C, McInerney DJ. 175.  2012. Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods. Risk Anal.: Int. J. 32:101657–72 [Google Scholar]
  176. Korteling B, Dessai S, Kapelan Z. 176.  2013. Using information-gap decision theory for water resources planning under severe uncertainty. Water Resour. Manag. 27:41149–72 [Google Scholar]
  177. Takewaki I.177.  2013. Toward greater building earthquake resilience using concept of critical excitation: a review. Sustain. Cities Soc. 9:39–53 [Google Scholar]
  178. Herman JD, Reed P, Characklis G. 178.  2015. How should robustness be defined for water systems planning under change?. J. Water Resour. Plan. Manag. 141:1004015012 [Google Scholar]
  179. Giuliani M, Castelletti A. 179.  2016. Is robustness really robust? How different definitions of robustness impact decision-making under climate change. Clim. Change 135:3409–24 [Google Scholar]
  180. Wilby R, Troni J, Biot Y, Tedd L, Hewitson BC. 180.  et al. 2009. A review of climate risk information for adaptation and development planning. Int. J. Climatology 29:91193–215 [Google Scholar]
  181. Payo A, Becker P, Otto A, Vervoort J, Kingsborough A. 181.  2015. Experiential lock-in: characterizing avoidable maladaptation in infrastructure systems. J. Infrastructure Syst. 22:1 [Google Scholar]
  182. Dixit AK, Pindyck RS. 182.  1994. Investment Under Uncertainty Princeton, NJ: Princeton Univ. Press
  183. Haasnoot M, Middelkoop H, Offermans A, van Beek E, van Deursen PA. 183.  2012. Exploring pathways for sustainable water management in river deltas in a changing environment. Clim. Change 115:3795–819 [Google Scholar]
  184. Yohe G, Leichenko R. 184.  2010. Adopting a risk-based approach. Ann. N. Y. Acad. Sci.1196129–40
  185. Siebentritt M, Halsey N, Stafford-Smith M. 185.  2014. Regional climate change adaptation plan for the Eyre Peninsula. Prepared for Eyre Peninsula Integrated Climate Change Agreement Committee. http://www.naturalresources.sa.gov.au/files/sharedassets/eyre_peninsula/corporate/climate-change-adaptation-2014-plan.pdf
  186. Barnett J, Graham S, Mortreux C, Fincher R, Waters E, Hurlimann A. 186.  2014. A local coastal adaptation pathway. Nat. Clim. Change 4:121103–8 [Google Scholar]
  187. Lawrence J, Reisinger A, Mullan B, Jackson B. 187.  2013. Exploring climate change uncertainties to support adaptive management of changing flood-risk. Environ. Sci. Policy 33:133–42 [Google Scholar]
  188. Koutsoyiannis D.188.  2011. Hurst-Kolmogorov dynamics and uncertainty. J. Am. Water Resour. Assoc. 47:3481–95 [Google Scholar]

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