Climatic Change

, Volume 116, Issue 3–4, pp 631–663 | Cite as

Projections of climate change impacts on river flood conditions in Germany by combining three different RCMs with a regional eco-hydrological model

  • Shaochun Huang
  • Fred F. Hattermann
  • Valentina Krysanova
  • Axel Bronstert


A general increase in precipitation has been observed in Germany in the last century, and potential changes in flood generation and intensity are now at the focus of interest. The aim of the paper is twofold: a) to project the future flood conditions in Germany accounting for various river regimes (from pluvial to nival-pluvial regimes) and under different climate scenarios (the high, A2, low, B1, and medium, A1B, emission scenarios) and b) to investigate sources of uncertainty generated by climate input data and regional climate models. Data of two dynamical Regional Climate Models (RCMs), REMO (REgional Model) and CCLM (Cosmo-Climate Local Model), and one statistical-empirical RCM, Wettreg (Wetterlagenbasierte Regionalisierungsmethode: weather-type based regionalization method), were applied to drive the eco-hydrological model SWIM (Soil and Water Integrated Model), which was previously validated for 15 gauges in Germany. At most of the gauges, the 95 and 99 percentiles of the simulated discharge using SWIM with observed climate data had a good agreement with the observed discharge for 1961–2000 (deviation within ±10 %). However, the simulated discharge had a bias when using RCM climate as input for the same period. Generalized Extreme Value (GEV) distributions were fitted to the annual maximum series of river runoff for each realization for the control and scenario periods, and the changes in flood generation over the whole simulation time were analyzed. The 50-year flood values estimated for two scenario periods (2021–2060, 2061–2100) were compared to the ones derived from the control period using the same climate models. The results driven by the statistical-empirical model show a declining trend in the flood level for most rivers, and under all climate scenarios. The simulations driven by dynamical models give various change directions depending on region, scenario and time period. The uncertainty in estimating high flows and, in particular, extreme floods remains high, due to differences in regional climate models, emission scenarios and multi-realizations generated by RCMs.


Regional Climate Model Emission Scenario Shuttle Radar Topographic Mission Generalize Extreme Value Generalize Extreme Value Distribution 



The authors would like to thank the German Federal Ministry of Education and Research (BMBF) and the Berlin-based German Insurance Association (GDV), who supported this work as one part of the GDV research project (Climate impact on the damage situation in the German insurance system). Many thanks go also to the colleagues at PIK who supported the work and especially the set-up of the data bases, namely Hermann Österle and Tobias Vetter.


  1. Arnold JG, Williams JR, Nicks AD, Sammons NB (1990) SWRRB—A basin scale simulation model for soil and water resources management. Texas A&M University Press, College Station, p 255Google Scholar
  2. Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142:47–69CrossRefGoogle Scholar
  3. Booij MJ (2002) Modelling the effects of spatial and temporalresolution of rainfall and basin model on extreme river discharge. Hydrol Sci J 47(2):307–320CrossRefGoogle Scholar
  4. Bronstert A (2003) Floods and climate change: interactions and impacts. Risk Anal 23(3):545–557CrossRefGoogle Scholar
  5. Bronstert A, Bárdossy A, Bismuth C, Buiteveld H, Disse M, Engel H, Fritsch U, Hundecha Y, Lammersen R, Niehoff D, Ritter N (2007a) Multi-scale modelling of land-use change and river training effects on floods in the Rhine basin. River Res Appl 23(10):1102–1125CrossRefGoogle Scholar
  6. Bronstert A, Kolokotronis V, Schwandt D, Straub H (2007b) Comparison and evaluation of regional climate scenarios for hydrological impact analysis: General scheme and application example. Int J Clim 27:1579–1594CrossRefGoogle Scholar
  7. Bürger G (2009) Dynamically vs. empirically downscaled medium-range precipitation forecasts. Hydrol Earth Syst Sci 13:1649–1658CrossRefGoogle Scholar
  8. Cameron D (2006) An application of the UKCIP02 climate change scenarios to flood estimation by continuous simulation for a gauged catchment in the northeast of Scotland, UK (with uncertainty). J Hydrol 328:212–226CrossRefGoogle Scholar
  9. Christensen JH, Boberg F, Christensen OB, Lucas-Picker P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35. doi: 10.1029/2008GL035694
  10. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, LondonGoogle Scholar
  11. Dankers R, Christensen OB, Feyen L, Kalas M, de Roo A (2007) Evaluation of very high-resolution climate model data for simulating flood hazards in the Upper Danube Basin. J Hydrol 347:319–331CrossRefGoogle Scholar
  12. Disse M, Engel H (2001) Flood events in the Rhine basin: genesis, influences and mitigation. Nat Hazards 23(2–3):271–290. doi: 10.1023/A:1011142402374 CrossRefGoogle Scholar
  13. Doherty J (2004) PEST: model independent parameter estimation. Watermark Numerical Computing, Brisbane, Fifth edition of user manualGoogle Scholar
  14. Enke W, Deutschlaender T, Schneider F, Kuechler W (2005a) Results of five regional climate studies applying a weather pattern based downscaling method to ECHAM4 climate simulations. Meteorol Z 14(2):247–257CrossRefGoogle Scholar
  15. Enke W, Schneider F, Deutschlaender T (2005b) A novel scheme to derive optimized circulation pattern classifications for downscaling and forecast purposes. Theor Appl Climatol 82:51–63CrossRefGoogle Scholar
  16. Gelfan AN, Pomeroy JW, Kuchment LS (2004) Modelling forest cover influences on snow accumulation, sublimation, and melt. J Hydrometeorol 5(5):785–803CrossRefGoogle Scholar
  17. Graham LP, Andreasson J, Carlsson B (2007) Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods—a case study on the Lule River basin. Clim Change 81:293–307CrossRefGoogle Scholar
  18. Grünewald U, Kaltofen M, Rolland W, Schümberg S, Chmielewski R, Ahlheim M, Sauer T, Wagner R, Schluchter W, Birkner H, Petzold R, Radczuk L, Eliasiewicz R, Paus L, Zahn G (1998) Ursachen, Verlauf und Folgen des Sommer-Hochwassers 1997 an der Oder sowie Aussagen zu bestehenden Risikopotentialen. Eine interdisziplinäre Studie. Deutsches IDNDR-Komitee für Katastrophenvorbeugung e. V., IDNDR-Reihe H. 10b (long version), Bonn, p 187Google Scholar
  19. Hattermann FF, Krysanova V, Wechsung F, Wattenbach M (2005) Runoff simulations on the macroscale with the ecohydrological model SWIM in the Elbe catchment—validation and uncertainty analysis. Hydrol Process 19:693–714CrossRefGoogle Scholar
  20. Hay LE, Wilby RL, Leavesley GH (2000) A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J Am Water Resour Assoc 36:387–398CrossRefGoogle Scholar
  21. Haylock MR, Cawley GC, Harpham C, Wilby RL, Goodess CM (2006) Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios. Int J Climatol 26(10):1397–1415CrossRefGoogle Scholar
  22. Huang S, Krysanova V, Österle H, Hattermann FF (2010) Simulation of spatiotemporal dynamics of water fluxes in Germany under climate change. Hydrol Process. doi: 10.1002/hyp.7753
  23. IPCC (2007) Climate Change 2007: impacts, adaptation and vulnerability—summary for policymakers. Working Group II Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC SecretariatGoogle Scholar
  24. Jacob D (2001) A note on the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainige basin. Meteorol Atmos Phys 77:61–73CrossRefGoogle Scholar
  25. Kay AL, Jones RG, Reynard NS (2006) RCM rainfall for UK flood frequency estimation. II. Climate change results. J Hydrol 318:163–172CrossRefGoogle Scholar
  26. Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Chang 92:41–63CrossRefGoogle Scholar
  27. Kleinn J, Frei C, Gurtz J, Lüthi D, Vidale PL, Schär C (2005) Hydrologic simulations in the Rhine basin driven by a regional climate model. J Geophys Res 110:D04102. doi: 10.1029/2004JD005143 CrossRefGoogle Scholar
  28. Kosková R, Nemecková S, Hesse C (2007) Using of the soil parametrisation based on soil samples databases in rainfall-runoff modelling. In: Jakubíková A, Broza V, Szolgay J (eds) Proceedings of the Adolf Patera workshop “Extreme hydrological events in catchments”. 13.11.2007. Bratislava, pp 241–249. ISBN 978-80-01-03960-1 [in czech]Google Scholar
  29. Kreibich H, Merz B, Grünewald U (2007) Lessons learned from the Elbe River floods in August 2002—with a special focus on flood warning. Extreme hydrological events: new concepts for security. NATO Sci Ser 78(1):69–80. doi: 10.1007/978-1-4020-5741-0_5 CrossRefGoogle Scholar
  30. Krysanova V, Meiner A, Roosaare J, Vasilyev A (1989) Simulation modelling of the coastal waters pollution from agricultural watersheds. Ecol Model 49:7–29CrossRefGoogle Scholar
  31. Krysanova V, Müller-Wohlfeil DI, Becker A (1998) Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds. Ecol Model 106:261–289CrossRefGoogle Scholar
  32. Krysanova V, Hattermann FF, Wechsung F (2007) Implications of complexity and uncertainty for integrated modelling and impact assessment in river basins. Environ Model Softw 22:701–709CrossRefGoogle Scholar
  33. Leander R, Buishand TA, van den Hurk BJJM, de Wit MJM (2008) Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output. J Hydrol 351:331–343CrossRefGoogle Scholar
  34. Lenderink G, Buishand A, van Deursen W (2007) Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrol Earth Syst Sci 11(3):1145–1159CrossRefGoogle Scholar
  35. Lopes VL (1996) On the effect of uncertainty in spatial distribution of rainfall on catchment modeling. Catena 28:107–119CrossRefGoogle Scholar
  36. Majewski D (1991) The Europa Modell of the Deutscher Wetterdienst. ECMWF Semin Numer Methods Atmos Model 2:147–191Google Scholar
  37. Menzel L, Bürger G (2002) Climate change scenarios and runoff response in the Mulde catchment (Southern Elbe, Germany). J Hydrol 267:53–64CrossRefGoogle Scholar
  38. Menzel L, Thieken AH, Schwandt D, Bürger G (2006) Impact of climate change on the regional hydrology—scenario-based modelling studies in the German Rhine catchment. Nat Hazards 38:45–61CrossRefGoogle Scholar
  39. Middelkoop H, Kwadijk JCJ (2001) Towards integrated assessment of the implications of global change for water management—The Rhine experience. Phys Chem Earth (B) 26(7—8):553–560CrossRefGoogle Scholar
  40. Mikhailov VN, Morozov VN, Cheroy NI, Mikhailova MV, YeF Z’y (2008) Extreme flood on the Danube River in 2006. Russ Meterol Hydrol 33(1):48–54CrossRefGoogle Scholar
  41. Monteith JL (1965) Evaporation and the environment. Symp Soc Exp Biol 19:205–234Google Scholar
  42. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I: a discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  43. Petrow T, Merz B (2009) Trends in flood magnitude, frequency and seasonality in Germany in the period 1951–2002. J Hydrol 371:129–141CrossRefGoogle Scholar
  44. Petrow T, Zimmer J, Merz B (2009) Changes in the flood hazard in Germany through changing frequency and persistence of circulation patterns. Nat Hazards Earth Syst Sci 9:1409–1423CrossRefGoogle Scholar
  45. Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large scale parameters. Mon Weather Rev 100:81–92CrossRefGoogle Scholar
  46. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorol Z 17(4):347–348CrossRefGoogle Scholar
  47. Samaniego L, Bárdossy A (2007) Relating macroclimatic circulation patterns with characteristics of floods and droughts at the mesoscale. J Hydrol 335:109–123CrossRefGoogle Scholar
  48. Schmidli J, Goodess CM, Frei C, Haylock MR, Hundecha Y, Ribalaygua J, Schmith T (2007) Statistical and dynamical downscaling of precipitation: an evaluation and comparison of scenarios for the European Alps. J Geophys Res 112:D04105. doi: 10.1029/2005JD007026 CrossRefGoogle Scholar
  49. Schönwiese CD, Staeger T, Trömel S (2006) Klimawandel und Extremereignisse in Deutschland. Klimastatusbericht 2005. Deutscher Wetterdienst, Offenbach, S., pp 7–17Google Scholar
  50. Shabalova MV, van Deursen WPA, Buishand TA (2003) Assessing future discharge of the river Rhine using regional climate model integrations and a hydrological model. Clim Res 23:233–246CrossRefGoogle Scholar
  51. Steppeler J, Doms G, Schaettler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol Atmos Phys 82:75–96CrossRefGoogle Scholar
  52. Suklitsch M, Gobiet A, Truhetz H, Awan NK, Göttel H, Jacob D (2010) Error characteristics of high resolution regional climate models over Alpine area. Clim Dyn. doi: 10.1007/s00382-010-0848-5
  53. Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correcton of daily precipitation from regional climate models. Int J Climatol. doi: 10.1002/joc.2168
  54. Van Ulden AP, van Oldenborgh GJ (2006) Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for climate change in Central Europe. Atmos Chem Phys 6:863–881CrossRefGoogle Scholar
  55. Varis O, Kajander T, Lemmelä R (2004) Climate change and water: from climate models to water resources management and vice versa. Clim Chang 66:321–344CrossRefGoogle Scholar
  56. Williams JR, Renard KG, Dyke PT (1984) EPIC—a new model for assessing erosion’s effect on soil productivity. J Soil Water Conserv 38(5):381–383Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Shaochun Huang
    • 1
  • Fred F. Hattermann
    • 1
  • Valentina Krysanova
    • 1
  • Axel Bronstert
    • 1
    • 2
  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  2. 2.Chair for Hydrology and ClimatologyUniversity of PotsdamPotsdam-GolmGermany

Personalised recommendations