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Climatic Change

, Volume 151, Issue 3–4, pp 491–506 | Cite as

Decomposing the cascade of uncertainty in risk assessments for urban flooding reflecting critical decision-making issues

  • Kirsten Halsnæs
  • Per Skougaard Kaspersen
Article

Abstract

Climate change risk assessments traditionally follow an analytical structure in which climate information is linked to impact models, and subsequently to damage models and decision-making tools. This structure generates a wide cascade of uncertainties that accumulate with each analytical step, consequently resulting in a wide range of risk estimates. This cascade of uncertainties can suggest that climate change risk assessments are not very useful in the context of decision-making regarding climate adaptation. However, many of the uncertainties revealed in traditionally structured climate risk assessments are not equally relevant to specific decisions, and presenting wide cascades of uncertainties can mask key decision-making parameters. In this paper, we show how the cascade of uncertainty relevant to decision-making can be reduced by applying an uncertainty decomposition approach, which, in study design, initially identifies the uncertainty cascade elements of particular relevance to the focal decision-making context. We compare the full cascade of uncertainties that emerge in a traditional risk assessment based on linked climate scenarios, impact modeling, and damage cost assessment with the uncertainty cascade generated by a detailed assessment of urban flooding risks where the focus is on key uncertainties in decision-making on climate change adaptation. A case study on flooding from extreme precipitation in the Danish city of Odense is used to decompose major sources of uncertainties in the climate modeling, the hydrological modeling, and the damage cost assessment. The decomposition approach reduces the focal range of damage cost estimates by 7–9 M EUR, which corresponds to a 20–24% reduction in the full uncertainty range without the application of the decomposition approach. Assuming that damage cost assessments can provide an indication of how much society should be willing to spend on climate adaptation, a decomposition approach as presented here could assist decision-makers in increasing the economic effectiveness when investing in protective measures.

Notes

Acknowledgements

The authors thank Jakob Luchner and Nina Donna Domingo of DHI for assisting with the extreme value analysis and for technical support in constructing and running the MIKE 21 overland flow model.

Supplementary material

10584_2018_2323_MOESM1_ESM.docx (1.6 mb)
ESM 1 (DOCX 1643 kb)

References

  1. Chambwera M, Heal G, Dubeux C, Hallegatte S, Leclerc L, Markandya A, McCarl BA, Mechler R, Neumann JE (2014) Economics of adaptation. In: 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. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) . Cambridge University Press, Cambridge, United Kingdom and New York, pp 945–977Google Scholar
  2. Climate Policy Initiative (2017) Global landscapes of climate finance, https://climatepolicyinitiative.org/publication/global-landscape-of-climate-finance-2015/ . Accessed 22 March 2017
  3. Cubasch, U., D. Wuebbles, D. Chen, M.C. Facchini, D. Frame, N. Mahowald, and J.-G. Winther, 2013: Introduction. In: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  4. Danish Tax Authority (2017) Ejendomsvurdering. http://www.vurdering.skat.dk/Ejendomsvurdering. Accessed 2017
  5. DHI (2017) MIKE 21 - powered by DHI. https://www.mikepoweredbydhi.com/products/mike-21
  6. EEA (2013) Digital elevation model over Europe (EU-DEM), European Energy Agency. http://www.eea.europa.eu/data-and-maps/data/eu-dem. Accessed 24 August 2017
  7. ESGF (2016) WCRP CORDEX. https://esgf-node.ipsl.upmc.fr/search/cordex-ipsl/. Accessed 15 January 2016
  8. European Environment Agency (2017) Climate change, impacts and vulnerability in Europe 2016: an indicator-based reportGoogle Scholar
  9. Fereday D, Chadwick R, Knight J, Scaife AA (2018) Atmospheric dynamics is the largest source of uncertainty in future winter European rainfall. J Clim 31:963–977.  https://doi.org/10.1175/JCLI-D-17-0048.1 CrossRefGoogle Scholar
  10. Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (2012) Managing the risks of extreme events and disasters to advance climate change adaption: a special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, New YorkCrossRefGoogle Scholar
  11. Halsnæs K, Kaspersen P, Drews M (2015) Key drivers and economic consequences of high end climate scenarios: uncertainties and risks. Clim Res.  https://doi.org/10.3354/cr01308
  12. Haasnoot M, Kwakkel JH, Walker WE, ter Maat J (2013) Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob Environ Change 23:485–498.  https://doi.org/10.1016/j.gloenvcha.2012.12.006 CrossRefGoogle Scholar
  13. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418.  https://doi.org/10.1007/s00382-010-0810-6 CrossRefGoogle Scholar
  14. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107.  https://doi.org/10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  15. Intergovernmental Panel on Climate Change (Ed.) (2014) Climate Change 2013: The physical science basis. Working group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  16. Jongman B, Kreibich H, Apel H, Barredo JI, Bates PD, Feyen L, Gericke A, Neal J, Aerts JCJH, Ward PJ (2012) Comparative flood damage model assessment: towards a European approach. Nat Hazards Earth Syst Sci 12:3733–3752.  https://doi.org/10.5194/nhess-12-3733-2012 CrossRefGoogle Scholar
  17. Kaspersen P, Fensholt R, Drews M (2015) Using Landsat vegetation indices to estimate impervious surface fractions for European cities. Remote Sens 7:8224–8249.  https://doi.org/10.3390/rs70608224 CrossRefGoogle Scholar
  18. Kaspersen PS, Halsnæs K (2017) Integrated climate change risk assessment: a practical application for urban flooding during extreme precipitation. Clim Serv.  https://doi.org/10.1016/j.cliser.2017.06.012
  19. Kent C, Chadwick R, Rowell DP (2015) Understanding uncertainties in future projections of seasonal tropical precipitation. J Clim 28:4390–4413.  https://doi.org/10.1175/JCLI-D-14-00613.1 CrossRefGoogle Scholar
  20. Kortforsyningen.dk (2017a) Nedbør: Hydraulisk ledningsevne. https://download.kortforsyningen.dk/content/nedb%C3%B8r-hydraulisk-ledningsevne . Accessed 17 July 2017
  21. Kortforsyningen.dk (2017b) Værdikort for bygninger. https://download.kortforsyningen.dk/content/v%C3%A6rdikort-v%C3%A6rdikort-bygninger. Accessed 17 July 2017
  22. Madsen MS, Langen PL, Boberg F, Christensen JH (2017) Inflated uncertainty in multimodel-based regional climate projections: inflated uncertainty. Geophys Res Lett 44:11,606–11,613.  https://doi.org/10.1002/2017GL075627 CrossRefGoogle Scholar
  23. Maraun D (2013) When will trends in European mean and heavy daily precipitation emerge? Environ Res Lett 8(1):014004.  https://doi.org/10.1088/1748-9326/8/1/014004 CrossRefGoogle Scholar
  24. Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, Matsumoto K, Montzka SA, Raper SCB, Riahi K, Thomson A, Velders GJM, van Vuuren DPP (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Chang 109:213–241.  https://doi.org/10.1007/s10584-011-0156-z CrossRefGoogle Scholar
  25. Moss RH, Schneider SH (2000) Uncertainties in the IPCC TAR: recommendations to lead authors for more consistent assessment and reporting. In: Pachauri R, Taniguchi T, Tanaka K (eds) Guidance papers on the cross cutting issues of the third assessment report of the IPCC. World Meteorological Organization, Geneva, pp 33–51Google Scholar
  26. Munich RE (2017): Topics geo - natural catastrophes (2016) analyses, assessments, positions 2017 issueGoogle Scholar
  27. Sanford T, Frumhoff PC, Luers A, Gulledge J (2014) The climate policy narrative for a dangerously warming world. Nat Clim Chang 4:164CrossRefGoogle Scholar
  28. Santos JA, Belo-Pereira M, Fraga H, Pinto JG (2016) Understanding climate change projections for precipitation over western Europe with a weather typing approach: precipitation projections for Europe. J Geophys Res Atmospheres 121:1170–1189.  https://doi.org/10.1002/2015JD024399 CrossRefGoogle Scholar
  29. Schneider SH (1983) CO2 climate and society: a brief overview. Social science research and climate change an interdisciplinary appraisal. Springer VerlagGoogle Scholar
  30. Skougaard Kaspersen P, Høegh Ravn N, Arnbjerg-Nielsen K, Madsen H, Drews M (2017) Comparison of the impacts of urban development and climate change on exposing European cities to pluvial floodingGoogle Scholar
  31. Stocker TF, Qin D, Plattner GK, Alexander LV, Allen SK, Bindoff NL, Bréon FM, Church JA, Cubasch U, Emori S, Forster P, Friedlingstein P, Gillett N, Gregory JM, Hartmann DL, Jansen E, Kirtman B, Knutti R, Krishna Kumar K, Lemke P, Marotzke J, Masson-Delmotte V, Meehl GA, Mokhov II, Piao S, Ramaswamy V, Randall D, Rhein M, Rojas M, Sabine C, Shindell D, Talley LD, Vaughan DG and Xie SP (2013) Technical summary. In: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V and Midgley PM (eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  32. Sunyer MA, Hundecha Y, Lawrence D, Madsen H, Willems P, Martinkova M, Vormoor K, Bürger G, Hanel M, Kriaučiūnienė J, Loukas A, Osuch M, Yücel I (2015) Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe. Hydrol Earth Syst Sci 19:1827–1847.  https://doi.org/10.5194/hess-19-1827-2015 CrossRefGoogle Scholar
  33. van den Hoek RE, Brugnach M, Mulder JPM, Hoekstra AY (2014) Analysing the cascades of uncertainty in flood defence projects: how “not knowing enough” is related to “knowing differently”. Glob. Environ. Change 24:373–388.  https://doi.org/10.1016/j.gloenvcha.2013.11.008 CrossRefGoogle Scholar
  34. Weitzman ML (2011) Fat-tailed uncertainty in the economics of catastrophic climate change. Rev Environ Econ Policy 5:275–292.  https://doi.org/10.1093/reep/rer006 CrossRefGoogle Scholar
  35. Weng Q (2001) Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS. Environ Manag 28:737–748.  https://doi.org/10.1007/s002670010258 CrossRefGoogle Scholar
  36. Wilby RL, Dessai S (2010) Robust adaptation to climate change. Weather 65:180–185.  https://doi.org/10.1002/wea.543 CrossRefGoogle Scholar
  37. Willems P, Olsson J, Arnbjerg-Nielsen K, Beecham S, Pathirana A, Gregersen I.B, Madsen H, Nguyen V (2012) Impacts of climate change on rainfall extremes and urban drainage systems. IWA, LondonGoogle Scholar
  38. Yang J-L, Zhang G-L (2011) Water infiltration in urban soils and its effects on the quantity and quality of runoff. J Soils Sediments 11:751–761.  https://doi.org/10.1007/s11368-011-0356-1 CrossRefGoogle Scholar
  39. Zhang X, Zwiers FW, Li G, Wan H, Cannon AJ (2017) Complexity in estimating past and future extreme short-duration rainfall. Nat Geosci 10:255–259.  https://doi.org/10.1038/ngeo2911 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Climate Change and Sustainable Development (CCSD) Research Group, Department of Management EngineeringTechnical University of DenmarkKgs. LyngbyDenmark

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