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Communicating and Using Ensemble Flood Forecasts in Flood Incident Management: Lessons from Social Science

  • David DemerittEmail author
  • Elisabeth M. Stephens
  • Laurence Créton-Cazanave
  • Céline Lutoff
  • Isabelle Ruin
  • Sébastien Nobert
Reference work entry

Abstract

This chapter explores the practical challenges of communicating and using ensemble forecasts in operational flood incident management. It reviews recent social science research on the variety and effectiveness of hydrological ensemble prediction systems (HEPS) visualization methods and on the cognitive and other challenges experienced by forecast recipients in understanding probabilistic forecasts correctly. To explore how those generic findings from the research literature work out in actual operational practice, the chapter then discusses a series of case studies detailing the development, communication, and use of HEPS products in various institutional contexts in France, Britain, and internationally at the EU and global levels. The chapter concludes by drawing out some broader lessons from those experiences about how to communicate and use HEPS more effectively.

Keywords

Risk communication Risk perception Cognitive biases Heuristics Visualization Spaghetti plots HEPS, public saliency of Decision-making Probability of precipitation forecasts Uncertainty, public understanding of Civil protection EFAS GloFAS SCHAPI UK Met Office Environment Agency 

References

  1. L. Alfieri, P. Burek, E. Dutra et al., GloFAS – global ensemble streamflow forecasting and flood early warning. Hydrol. Earth Syst. Sci. 17, 1161–1175 (2013).  https://doi.org/10.5194/hess-17-1161-2013CrossRefGoogle Scholar
  2. BBC, Money “no object” for flood relief. BBC News (2014). http://www.bbc.com/news/uk-26131515. Accessed 1 Aug 2014
  3. H. Bell, G. Tobin, Efficient and effective? The 100-year flood in the communication and perception of flood risk. Environ. Hazards 7, 302–311 (2007).  https://doi.org/10.1016/j.envhaz.2007.08.004CrossRefGoogle Scholar
  4. G. Blöschl, Flood warning – on the value of local information. Int. J. River Basin Manage. 6, 41–50 (2008).  https://doi.org/10.1080/15715124.2008.9635336CrossRefGoogle Scholar
  5. K. Bogner, F. Pappenberger, Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system. Water Resour. Res. 47, W07524 (2011).  https://doi.org/10.1029/2010WR009137CrossRefGoogle Scholar
  6. K. Broad, A. Leiserowitz, J. Weinkle, M. Steketee, Misinterpretations of the “Cone of Uncertainty” in Florida during the 2004 Hurricane season. Bull. Am. Meteorol. Soc. 88, 651–667 (2007).  https://doi.org/10.1175/BAMS-88-5-651CrossRefGoogle Scholar
  7. M. Bruen, P. Krahe, M. Zappa et al., Visualizing flood forecasting uncertainty: some current European EPS platforms-COST731 working group 3. Atmos. Sci. Lett. 11, 92–99 (2010).  https://doi.org/10.1002/asl.258CrossRefGoogle Scholar
  8. P. Bye, M. Horner, Easter 1998 Floods: Report by the Independent Review Team to the Board of the Environment Agency (Environment Agency, Bristol, 1998)Google Scholar
  9. CEC, The European Community Response to the Flooding in Austria, Germany and Several Applicant Countries: A solidarity-Based Initiative (Commission of the European Communities, Brussels, 2002)Google Scholar
  10. H.L. Cloke, F. Pappenberger, Ensemble flood forecasting: a review. J. Hydrol. 375, 613–626 (2009).  https://doi.org/10.1016/j.jhydrol.2009.06.005CrossRefGoogle Scholar
  11. A. Cole, Prefects in search of a role in a Europeanised France. J. Public Policy 31, 385–407 (2011).  https://doi.org/10.1017/S0143814X11000122CrossRefGoogle Scholar
  12. H.M. Collins, R. Evans, The third wave of science studies: studies of expertise and experience. Soc. Stud. Sci. 32, 235–296 (2002).  https://doi.org/10.1177/0306312702032002003CrossRefGoogle Scholar
  13. E. Coughlan de Perez, B. van den Hurk, M.K. van Aalst et al., Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts. Nat. Hazards Earth Syst. Sci. 15, 895–904 (2015).  https://doi.org/10.5194/nhess-15-895-2015CrossRefGoogle Scholar
  14. L. Créton-Cazanave, Warning! The use of meteorological information during a flash-flood warning process. Adv. Sci. Res. 3, 99–103 (2009)CrossRefGoogle Scholar
  15. L. Créton-Cazanave, Penser l’alerte par les distances. Entre planification et émancipation, l’exemple du processus d’alerte aux crues rapides sur le bassin versant du Vidourle, Université de Grenoble, 2010Google Scholar
  16. L. Créton-Cazanave, C. Lutoff, Stakeholders’ issues for action during the warning process and the interpretation of forecasts’ uncertainties. Nat. Hazards Earth Syst. Sci. 13, 1469–1479 (2013).  https://doi.org/10.5194/nhess-13-1469-2013CrossRefGoogle Scholar
  17. J.D. Creutin, M. Borga, E. Gruntfest et al., A space and time framework for analyzing human anticipation of flash floods. J. Hydrol. 482, 14–24 (2013).  https://doi.org/10.1016/j.jhydrol.2012.11.009CrossRefGoogle Scholar
  18. M. Dale, J. Wicks, K. Mylne et al., Probabilistic flood forecasting and decision-making: an innovative risk-based approach. Nat. Hazards 70, 159–172 (2014).  https://doi.org/10.1007/s11069-012-0483-zCrossRefGoogle Scholar
  19. F. Daupras, J.M. Antoine, S. Becerra, A. Peltier, Analysis of the robustness of the French flood warning system: a study based on the 2009 flood of the Garonne River. Nat. Hazards 1–27 (2014).  https://doi.org/10.1007/s11069-014-1318-xCrossRefGoogle Scholar
  20. A. De Roo, J. Thielen, P. Salamon et al., Quality control, validation and user feedback of the European Flood Alert System (EFAS). Int. J. Digital Earth 4, 77–90 (2011).  https://doi.org/10.1080/17538947.2010.510302CrossRefGoogle Scholar
  21. F. Dedieu, Une catastrophe ordinaire la tempête du 27 décembre 1999 (Editions de l’Ecole des Hautes Etudes en Sciences Sociales, Paris, 2013)CrossRefGoogle Scholar
  22. D. Demeritt, The perception and use of public weather services by emergency and resiliency professionals in the UK. Report for the Met Office Public Weather Service Customer Group. 2 Mar (King’s College London, London, 2012)Google Scholar
  23. D. Demeritt, Spooked politicians are undermining flood defence policy with short term decisions. New Civil Engineer 9 (2014)Google Scholar
  24. D. Demeritt, S. Nobert, Responding to early flood warning in the European Union, in Forecasting, Warning, and Transnational Risks: Is Prevention Possible? ed. by C.O. Meyer, C. de Franco (Palgrave Macmillan, Basingstoke, 2011), pp. 127–147CrossRefGoogle Scholar
  25. D. Demeritt, S. Nobert, Models of best practice in flood risk communication and management. Environ. Hazards 1–16 (2014).  https://doi.org/10.1080/17477891.2014.924897CrossRefGoogle Scholar
  26. D. Demeritt, H. Cloke, F. Pappenberger et al., Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting. Environ. Hazards 7, 115–127 (2007).  https://doi.org/10.1016/j.envhaz.2007.05.001CrossRefGoogle Scholar
  27. D. Demeritt, S. Nobert, H. Cloke, F. Pappenberger, Challenges in communicating and using ensembles in operational flood forecasting. Meteorol. Appl. 17, 209–222 (2010).  https://doi.org/10.1002/met.194CrossRefGoogle Scholar
  28. D. Demeritt, S. Nobert, M. Buchecker et al., Assessing Risk Communication Strategies and Effectiveness in Early Warnings (UNESCO-IHE Institute for Water Education, Delft, 2013a)Google Scholar
  29. D. Demeritt, S. Nobert, H.L. Cloke, F. Pappenberger, The European Flood Alert System and the communication, perception, and use of ensemble predictions for operational flood risk management. Hydrol. Process. 27, 147–157 (2013b).  https://doi.org/10.1002/hyp.9419CrossRefGoogle Scholar
  30. C.A.I. Doswell, Weather forecasting by humans – heuristics and decision making. Weather Forecast. 19, 1115–1126 (2004)CrossRefGoogle Scholar
  31. G.A. Fine, Authors of the Storm: Meteorologists and the Culture of Prediction (University of Chicago Press, Chicago, 2010)Google Scholar
  32. J. Frick, C. Hegg, Can end-users’ flood management decision making be improved by information about forecast uncertainty? Atmos. Res. 100, 296–303 (2011).  https://doi.org/10.1016/j.atmosres.2010.12.006CrossRefGoogle Scholar
  33. G. Gigerenzer, Reckoning with Risk: Learning to Live with Uncertainty (Penguin Books, London, 2003)Google Scholar
  34. G. Gigerenzer, R. Hertwig, E. Van Den Broek et al., “A 30% chance of rain tomorrow”: how does the public understand probabilistic weather forecasts? Risk Anal. 25, 623–629 (2005).  https://doi.org/10.1111/j.1539-6924.2005.00608.xCrossRefGoogle Scholar
  35. GrrlScientist, O’Hara B, Is widespread sexism making hurricanes more deadly than himmicanes? (2014), http://www.theguardian.com/science/grrlscientist/2014/jun/04/hurricane-gender-name-bias-sexism-statistics?CMP=twt_fd. Accessed 5 Aug 2014
  36. S. Haines, E.M. Stephens, Partnerships in weather forecasting: development, distance, and dialogue (in review).Google Scholar
  37. J. Handmer, B. Proudley, Communicating uncertainty via probabilities: the case of weather forecasts. Environ. Hazards 7, 79–87 (2007).  https://doi.org/10.1016/j.envhaz.2007.05.002CrossRefGoogle Scholar
  38. W.E. Highfield, S.A. Norman, S.D. Brody, Examining the 100-year floodplain as a metric of risk, loss, and household adjustment. Risk Anal. 33, 186–191 (2013).  https://doi.org/10.1111/j.1539-6924.2012.01840.xCrossRefGoogle Scholar
  39. C. Hood, The Blame Game: Spin, Bureaucracy, and Self-Preservation in Government (Princeton University Press, Princeton, 2010)CrossRefGoogle Scholar
  40. F. Houdré, L’Annonce des crues: Histoire et évolution des services de 1847 à nos jours (Ministère de l’Aménagement du Territoire et de l’Environnement, Paris, 2001)Google Scholar
  41. M. Huber, H. Rothstein, The risk organisation: or how organisations reconcile themselves to failure. J. Risk Res. 16, 651–675 (2013).  https://doi.org/10.1080/13669877.2012.761276CrossRefGoogle Scholar
  42. P. Huet, P. Foin, C. Laurain, P. Cannard, Retour d’expe´rience des crues de septembre 2002 dans les départements du Gard, de l’Hérault, du Vaucluse, des Bouches-du-Rhône, de l’Ardèche et de la Drôme: rapport consolidé après phase contradictoire (Service de l’inspection générale de l’environnement, Paris, 2003)Google Scholar
  43. S.L. Joslyn, R.M. Nichols, Probability or frequency? Expressing forecast uncertainty in public weather forecasts. Meteorol. Appl. 16, 309–314 (2009).  https://doi.org/10.1002/met.121CrossRefGoogle Scholar
  44. S.L. Joslyn, L. Nadav-Greenberg, M.U. Taing, R.M. Nichols, The effects of wording on the understanding and use of uncertainty information in a threshold forecasting decision. Appl. Cogn. Psychol. 23, 55–72 (2009).  https://doi.org/10.1002/acp.1449CrossRefGoogle Scholar
  45. K. Jung, S. Shavitt, M. Viswanathan, J.M. Hilbe, Female hurricanes are deadlier than male hurricanes. Proc. Natl. Acad. Sci. U. S. A. 201402786 (2014).  https://doi.org/10.1073/pnas.1402786111CrossRefGoogle Scholar
  46. T. Kox, L. Gerhold, U. Ulbrich, Perception and use of uncertainty in severe weather warnings by emergency services in Germany. Atmos. Res. (2014).  https://doi.org/10.1016/j.atmosres.2014.02.024CrossRefGoogle Scholar
  47. R. Krzysztofowicz, The case for probabilistic forecasting in hydrology. J. Hydrol. 249, 2–9 (2001).  https://doi.org/10.1016/S0022-1694(01)00420-6CrossRefGoogle Scholar
  48. D. Lumbroso, B. von Christierson, Communication and Dissemination of Probabilistic Flood Warnings: Literature Review of International Material (Environment Agency, Bristol, 2009)Google Scholar
  49. F. Martini, A. De Roo, EXCIFF Guide: Good Practice for Delivering Flood-Related Information to the General Public (Joint Research Centre, European Commission, Ispra, 2007)Google Scholar
  50. S. MCarthy, S. Tunstall, D. Parker et al., Risk communication in emergency response to a simulated extreme flood. Environ. Hazards 7, 179–192 (2007).  https://doi.org/10.1016/j.envhaz.2007.06.003CrossRefGoogle Scholar
  51. C.O. Meyer, F. Otto, J. Brante, C. De Franco, Recasting the warning-response problem: persuasion and preventive policy. Int. Stud. Rev. 12, 556–578 (2010).  https://doi.org/10.1111/j.1468-2486.2010.00960.xCrossRefGoogle Scholar
  52. R.E. Morss, Interactions among flood predictions, decisions, and outcomes: synthesis of three cases. Nat. Hazard. Rev. 11, 83–96 (2009)CrossRefGoogle Scholar
  53. R.E. Morss, M.H. Hayden, Storm surge and “certain death”: interviews with Texas coastal residents following Hurricane Ike. Wea. Climate Soc. 2, 174–189 (2010).  https://doi.org/10.1175/2010WCAS1041.1CrossRefGoogle Scholar
  54. R.E. Morss, J.L. Demuth, J.K. Lazo, Communicating uncertainty in weather forecasts: a survey of the U.S. public. Weather Forecast. 23, 974–991 (2008).  https://doi.org/10.1175/2008WAF2007088.1CrossRefGoogle Scholar
  55. A.H. Murphy, S. Lichtenstein, B. Fischhoff, R.L. Winkler, Misinterpretations of precipitation probability forecasts. Bull. Am. Meteorol. Soc. 61, 695–701 (1980). https://doi.org/10.1175/1520-0477(1980)061<0695:MOPPF>2.0.CO;2CrossRefGoogle Scholar
  56. N. Nicholls, Cognitive illusions, heuristics, and climate prediction. Bull. Am. Meteorol. Soc. 80, 1385–1398 (1999)CrossRefGoogle Scholar
  57. S. Nobert, D. Demeritt, H. Cloke, Informing operational flood management with ensemble predictions: lessons from Sweden. J. Flood Risk Manage. 3, 72–79 (2010).  https://doi.org/10.1111/j.1753-318X.2009.01056.xCrossRefGoogle Scholar
  58. NRC [National Research Council], Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts (National Academies Press, Washington, DC, 2006)Google Scholar
  59. N.A. Odoni, S.N. Lane, Knowledge-theoretic models in hydrology. Prog. Phys. Geogr. 34, 151–171 (2010).  https://doi.org/10.1177/0309133309359893CrossRefGoogle Scholar
  60. T.N. Palmer, The economic value of ensemble forecasts as a tool for risk assessment: from days to decades. Q. J. Roy. Meteorol. Soc. 128, 747–774 (2002)CrossRefGoogle Scholar
  61. F. Pappenberger, H.L. Cloke, A. Persson, D. Demeritt, HESS Opinions “On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?”. Hydrol. Earth Syst. Sci. 15, 2391–2400 (2011).  https://doi.org/10.5194/hess-15-2391-2011CrossRefGoogle Scholar
  62. F. Pappenberger, E. Stephens, J. Thielen et al., Visualizing probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication. Hydrol. Process. 27, 132–146 (2013).  https://doi.org/10.1002/hyp.9253CrossRefGoogle Scholar
  63. E.C. Penning-Rowsell, S.M. Tunstall, S.M. Tapsell, D.J. Parker, The benefits of flood warnings: real but elusive, and politically significant. Water Environ. J. 14, 7–14 (2000)CrossRefGoogle Scholar
  64. M. Pitt, The Pitt Review: Learning Lessons from the 2007 Floods (Cabinet Office, London, 2008)Google Scholar
  65. M.-H. Ramos, T. Mathevet, J. Thielen, F. Pappenberger, Communicating uncertainty in hydro-meteorological forecasts: mission impossible? Meteorol. Appl. 17, 223–235 (2010).  https://doi.org/10.1002/met.202CrossRefGoogle Scholar
  66. M.S. Roulston, T.R. Kaplan, A laboratory-based study of understanding of uncertainty in 5-day site-specific temperature forecasts. Meteorol. Appl. 16, 237–244 (2009).  https://doi.org/10.1002/met.113CrossRefGoogle Scholar
  67. D. Spiegelhalter, M. Pearson, I. Short, Visualizing uncertainty about the future. Science 333, 1393–1400 (2011).  https://doi.org/10.1126/science.1191181CrossRefGoogle Scholar
  68. E. Stephens, H. Cloke, Improving flood forecasts for better flood preparedness in the UK (and beyond). Geogr. J. 4, 310–316 (2014).  https://doi.org/10.1111/geoj.12103CrossRefGoogle Scholar
  69. E.M. Stephens, T.L. Edwards, D. Demeritt, Communicating probabilistic information from climate model ensembles-lessons from numerical weather prediction. Wiley Interdiscip. Rev. Clim. Chang. 3, 409–426 (2012).  https://doi.org/10.1002/wcc.187CrossRefGoogle Scholar
  70. J. Thielen, J. Bartholmes, M.H. Ramos, A. De Roo, The European Flood Alert System – part 1: concept and development. Hydrol. Earth Syst. Sci. 13, 125 (2009)CrossRefGoogle Scholar
  71. G. Thirel, E. Martin, J.-F. Mahfouf et al., A past discharge assimilation system for ensemble streamflow forecasts over France – part 2: impact on the ensemble streamflow forecasts. Hydrol. Earth Syst. Sci. 14, 1639–1653 (2010a).  https://doi.org/10.5194/hess-14-1639-2010CrossRefGoogle Scholar
  72. G. Thirel, E. Martin, J.-F. Mahfouf et al., A past discharges assimilation system for ensemble streamflow forecasts over France – part 1: description and validation of the assimilation system. Hydrol. Earth Syst. Sci. 14, 1623–1637 (2010b).  https://doi.org/10.5194/hess-14-1623-2010CrossRefGoogle Scholar
  73. A. Tversky, D. Kahneman, Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974).  https://doi.org/10.1126/science.185.4157.1124CrossRefGoogle Scholar
  74. F. Vinet, Approches nationales de la prévention des risques et besoins locaux : le cas de la prévision et de l’alerte aux crues dans le Midi méditerranéen. Géocarrefour 82, 35–42 (2007).  https://doi.org/10.4000/geocarrefour.1438CrossRefGoogle Scholar
  75. F. Vinet, D. Lumbroso, S. Defossez, L. Boissier, A comparative analysis of the loss of life during two recent floods in France: the sea surge caused by the storm Xynthia and the flash flood in Var. Nat. Hazards 61, 1179–1201 (2012).  https://doi.org/10.1007/s11069-011-9975-5CrossRefGoogle Scholar
  76. V.H.M. Visschers, R.M. Meertens, W.W.F. Passchier, N.N.K. De Vries, Probability information in risk communication: a review of the research literature. Risk Anal. 29, 267–287 (2009).  https://doi.org/10.1111/j.1539-6924.2008.01137.xCrossRefGoogle Scholar
  77. J. Wilsdon, R. Willis, See-Through Science: Why Public Engagement Needs to Move Upstream (Demos, London, 2004)Google Scholar
  78. M. Zappa, F. Fundel, S. Jaun, A “Peak-Box” approach for supporting interpretation and verification of operational ensemble peak-flow forecasts. Hydrol. Process. 27, 117–131 (2013).  https://doi.org/10.1002/hyp.9521CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • David Demeritt
    • 1
    Email author
  • Elisabeth M. Stephens
    • 2
  • Laurence Créton-Cazanave
    • 3
  • Céline Lutoff
    • 4
  • Isabelle Ruin
    • 5
  • Sébastien Nobert
    • 6
  1. 1.Department of GeographyKing’s College LondonStrand, LondonUK
  2. 2.School of Archaeology, Geography and Environmental ScienceUniversity of ReadingWhiteknights, ReadingUK
  3. 3.Université Paris Est Marne-la-Vallée, Labex Futurs Urbains (LATTS, LEESU, Lab’Urba)Marne-la-ValléeFrance
  4. 4.Université Grenoble 1, PACTE UMR 5194 (CNRS, IEPG, UJF, UPMF)GrenobleFrance
  5. 5.Laboratoire d’étude des Transferts en Hydrologie et Environnement (LTHE)CNRSGrenobleFrance
  6. 6.School of Earth and EnvironmentUniversity of LeedsLeedsUK

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