Advertisement

Consensus by Simulation: a Flood Model for Participatory Policy Making

  • Lisa Brouwers
  • Mona Riabacke
Chapter
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 32)

Abstract

An overall goal of the Upper Tisza flood risk management project was to design a flood management policy that shared liability for disaster losses between the central government and individual households in a way that was considered acceptable by all the stakeholders. A participatory approach was adopted, where a flood simulation model was used interactively to support the process. In this chapter, we describe the design, implementation and use of the dynamic and spatially explicit flood simulation model, which incorporated novel elements like micro-level representation and Monte Carlo techniques. The model was provided with an interactive graphical interface designed to facilitate its use as a decision support tool in a participatory setting with multiple users. During this process, the model supported comparisons between pre-defined policy options, as well as the design of a new policy option on which consensus was finally reached.

Keywords

Catastrophe modeling Decision Support tool Flood risk management Flood simulation model Stakeholder processes Tisza 

References

  1. Amendola A, Ermoliev Y, Ermolieva T et al (2000a) A systems approach to modeling catastrophic risk and insurability. Nat Hazards 21(2/3):381–393CrossRefGoogle Scholar
  2. Amendola A, Ermoliev Y, Ermolieva T et al (2000b) Earthquake risk management: a case study for an Italian region. In: Proceedings of the second Euroconference on global change and catastrophe risk management: earthquake risks in Europe. IIASA, Laxenburg, Austria, 6–9 July 2000Google Scholar
  3. Cain J, Jinapala K, Makin IW et al (2003) Participatory decision support for agricultural management. A case study from Sri Lanka. Agr Syst 76:457–482CrossRefGoogle Scholar
  4. Dahinden U, Querol C, Jäger J et al (2000) Exploring the use of computer models in participatory integrated assessment – experiences and recommendations for further steps. Integr Assess 1:253–266CrossRefGoogle Scholar
  5. Ermolieva T (1997) The design of optimal insurance decisions in the presence of catastrophic risks. IIASA Interim report IR-97068, International Institute for Applied Systems Analysis, Laxenburg, AustriaGoogle Scholar
  6. Ermolieva T, Ermoliev Y (2005) Catastrophic risk management: flood and seismic risks case studies. In: Wallace SW, Ziemba WT (eds) Applications of stochastic programming, MPS-SIAM series on optimization. SIAM, PhiladelphiaGoogle Scholar
  7. Ermolieva T, Ermoliev Y, Fischer G et al (2003) The role of financial instruments in integrated catastrophic flood management. Multinatl Financ 7(3/4):207–230Google Scholar
  8. Friedman B (2004) Value sensitive design. In: Bainbridge WS (ed) Encyclopedia of human-computer interaction. Berkshire Publishing Group, Great Barrington, pp 769–774Google Scholar
  9. Gregory R, Fischhoff B, McDaniels T (2005) Acceptable input: using decision analysis to guide public policy deliberations. Decis Anal 2(1):4–16CrossRefGoogle Scholar
  10. Intergovernmental Panel on Climate Change (IPCC) Working Group III (2000) Emission scenarios: summary for policymakers. Tech rep ISBN: 92-9169-113-5, IPPC (WMO UNEP)Google Scholar
  11. Jakeman AJ, Letcher RA, Norton RA (2006) Ten iterative steps in development and evaluation of environmental models. Environ Model Software 21:602–614CrossRefGoogle Scholar
  12. Jiggins JLS, de Zeeuw H (1992) Participatory technology development in practice: process and methods. In: Reijntjes C, Haverkort B, Waters-Bayer A (eds) Farming for the future; an introduction to low-external-input and sustainable agriculture. Macmillan, LondonGoogle Scholar
  13. KSH (2000) Major annual figures – regional. Hungarian Statistics Central Office, BudapestGoogle Scholar
  14. Linnerooth-Bayer J, Amendola A (2000) Global change, catastrophic risk and loss spreading –issues of efficiency and equity. Geneva Pap Risk Insur 25(2):203–219CrossRefGoogle Scholar
  15. Linnerooth-Bayer J, Vari A, Thompson M (2006) Floods and fairness in Hungary. In: Verweij M, Thompson M (eds) Clumsy solutions for a complex world: governance politics and plural perceptions. Palgrave Macmillan, Basingstoke/New YorkGoogle Scholar
  16. Macintosh A, Whyte A (2006) Evaluating how eParticipation changes local democracy. In: IraniZ, Ghoneim A (eds) Proceedings of the eGovernment workshop 2006. Brunel University, London, eGov06Google Scholar
  17. Mitton L, Sutherland H, Weeks M (2000) Microsimulation modelling for policy analysis: challenges and innovations. Cambridge University Press, Cambridge/New YorkGoogle Scholar
  18. Rios Insua D, Kersten GE, Rios J et al (2007) Towards decision support for participatory democracy. Inf Syst E-Bus Manag 6(2):161–191CrossRefGoogle Scholar
  19. Stern PC, Fineberg HV (1996) Understanding risk – informing decision in a democratic society. National Academy Press, Washington, DCGoogle Scholar
  20. Vári A, Linnerooth-Bayer J, Ferencz Z (2003) Stakeholder views on flood risk management in Hungary’s Upper Tisza basin. Risk Anal 23:537–627CrossRefGoogle Scholar
  21. Vari A, Ereifej L, Ferencz Z (2009) Implementing the EU water framework directive in Hungary: a pilot project in the Upper-Tisza region. Int J Risk Assess Manag 12(1):82–102CrossRefGoogle Scholar
  22. VITUKI (1999) Consult Rt. Explanation of detailed methodology for flood damage assessment, BudapestGoogle Scholar
  23. Walker G (1997) Current developments in catastrophe modelling. In: Britton NR, Oliver J (eds) Financial risk management for natural catastrophes. Griffith University, BrisbaneGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of ICT, SCSKTH Royal Institute of TechnologyStockholm, KistaSweden
  2. 2.Department of Computer and Systems SciencesStockholm UniversityKista, StockholmSweden

Personalised recommendations