Climatic Change

, Volume 122, Issue 1–2, pp 171–184 | Cite as

Robust estimates of climate-induced hydrological change in a temperate mountainous region

  • N. KöplinEmail author
  • O. Rößler
  • B. Schädler
  • R. Weingartner


A sustainable water resources management depends on sound information about the impacts of climate change. This information is, however, not easily derived because natural runoff variability interferes with the climate change signal. This study presents a procedure that leads to robust estimates of magnitude and Time Of Emergence (TOE) of climate-induced hydrological change that also account for the natural variability contained in the time series. Firstly, natural variability of 189 mesoscale catchments in Switzerland is sampled for 10 ENSEMBLES scenarios for the control (1984–2005) and two scenario periods (near future: 2025–2046, far future: 2074–2095) applying a bootstrap procedure. Then, the sampling distributions of mean monthly runoff are tested for significant differences with the Wilcoxon-Mann–Whitney test and for effect size with Cliff’s delta d. Finally, the TOE of a climate change induced hydrological change is determined when at least eight out of the ten hydrological projections significantly differ from natural variability. The results show that the TOE occurs in the near future period except for high-elevated catchments in late summer. The significant hydrological projections in the near future correspond, however, to only minor runoff changes. In the far future, hydrological change is statistically significant and runoff changes are substantial. Temperature change is the most important factor determining hydrological change in this mountainous region. Therefore, hydrological change depends strongly on a catchment’s mean elevation. Considering that the hydrological changes are predicted to be robust in the near future highlights the importance of accounting for these changes in water resources planning.


Climate Scenario Future Period Hydrological Change Climate Change Signal Runoff Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was funded by the Swiss Federal Office for the Environment (FOEN). The authors would like to thank the FOEN, the Swiss Federal Statistical Office (SFSO) and the Federal Office for Meteorology and Climatology (MeteoSwiss) for providing the necessary input data. The delta change scenario data were distributed by the Center for Climate Systems Modeling (C2SM). The data were derived from regional climate simulations of the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged. The dataset has been prepared by Thomas Bosshard at ETH Zurich, partly funded by swisselectric/Swiss Federal Office of Energy (SFOE) and CCHydro/Swiss Federal Office for the Environment (FOEN). The authors would like to thank Frank Paul and Andreas Linsbauer, Institute of Geography, University of Zurich (GIUZ), for providing the scenarios of glacier retreat.

Supplementary material

10584_2013_1015_MOESM1_ESM.pdf (95 kb)
Fig. S1 (PDF 95 kb)
10584_2013_1015_MOESM2_ESM.pdf (440 kb)
Fig. S2 (PDF 439 kb)


  1. Arnell NW (2011) Uncertainty in the relationship between climate forcing and hydrological response in UK catchments. Hydrol Earth Syst Sci 15(3):897–912CrossRefGoogle Scholar
  2. Birsan M, Molnar P, Burlando P, Pfaundler M (2005) Streamflow trends in Switzerland. J Hydrol 314(1–4):312–329CrossRefGoogle Scholar
  3. Bosshard T, Kotlarski S, Ewen T, Schär C (2011) Spectral representation of the annual cycle in the climate change signal. Hydrol Earth Syst Sci 15(9):2777–2788CrossRefGoogle Scholar
  4. CH2011 (2011) Swiss Climate Change Scenarios CH2011. C2SM, MeteoSwiss, ETH, NCCR Climate, and OcCC, Zürich, Switzerland. Accessed 15 September 2013
  5. Cliff N (1993) Dominance statistics: ordinal analysis to answer ordinal questions. Psychol Bull 114:494–509CrossRefGoogle Scholar
  6. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26CrossRefGoogle Scholar
  7. Fatichi S, Rimkus S, Burlando P, Bordoy R, Molnar P (2013) Elevational dependence of climate change impacts on water resources in an Alpine catchment. Hydrol Earth Syst Sci Discuss 10(3):3743–3794CrossRefGoogle Scholar
  8. FOEN (2012) Effects of Climate Change on Water Resources and Waters. Synthesis report on “Climate Change and Hydrology in Switzerland” (CCHydro) project. Federal Office for the Environment, Bern. Umwelt-Wissen No 1217. Accessed 15 September 2013
  9. Giorgi F, Bi X (2009) Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys Res Lett 36(6), L06709Google Scholar
  10. Gurtz J, Baltensweiler A, Lang H (1999) Spatially distributed hydrotope-based modelling of evapotranspiration and runoff in mountainous basins. Hydrol Process 13:2751–2768CrossRefGoogle Scholar
  11. Hänggi P (2011) Auswirkungen der hydroklimatischen Variabilität auf die Wasserkraftnutzung in der Schweiz,PhD Thesis, Faculty of Science, University of Bern,Berne, Switzerland, 206 pp.Google Scholar
  12. Hawkins E, Sutton R (2012) Time of emergence of climate signals. Geophys Res Lett 39(1), L01702Google Scholar
  13. Hegerl G, Zwiers FW, Braconnot P, Gillett N, Luo Y, Marengo Orsini J, Nicholls N, Penner J, Stott P (eds) (2007) Understanding and Attributing Climate Change. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  14. Horton P, Schaefli B, Mezghani A, Hingray B, Musy A (2006) Assessment of climate-change impacts on alpine discharge regimes with climate model uncertainty. Hydrol Process 20(10):2091–2109CrossRefGoogle Scholar
  15. IPCC (2000) Emissions scenarios: A special report of IPCC Working Group III. Intergovernmental Panel on Climate Change, GenevaGoogle Scholar
  16. Köplin N, Schädler B, Viviroli D, Weingartner R (2012) Relating climate change signals and physiographic catchment properties to clustered hydrological response types. Hydrol Earth Syst Sci 16(7):2267–2283CrossRefGoogle Scholar
  17. Köplin N, Schädler B, Viviroli D, Weingartner R (2013) The importance of glacier and forest change in hydrological climate-impact studies. Hydrol Earth Syst Sci 17(2):619–635CrossRefGoogle Scholar
  18. Ledbetter R, Prudhomme C, Arnell N (2012) A method for incorporating climate variability in climate change impact assessments: sensitivity of river flows in the Eden catchment to precipitation scenarios. Clim Chang 113(3–4):803–823CrossRefGoogle Scholar
  19. Linsbauer A, Paul F, Machguth H, Haeberli W (2013) Comparing three different methods to model scenarios of future glacier change in the Swiss Alps. Ann Glaciol 54(63):241–253CrossRefGoogle Scholar
  20. Liu Y, Zhang J, Wang G, Liu J, He R, Wang H, Liu C, Jin J (2013) Assessing the effect of climate natural variability in water resources evaluation impacted by climate change. Hydrol Process 27(7):1061–1071CrossRefGoogle Scholar
  21. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60CrossRefGoogle Scholar
  22. Merz B, Maurer T, Kaiser K (2012) Wie gut können wir vergangene und zukünftige Veränderungen des Wasserhaushalts quantifizieren?: How well can we quantify past and future changes of the water cycle? Hydrol Wasserbewirtsch 56(5):244–256Google Scholar
  23. Storch H, Zwiers FW (2001) Statistical analysis in climate research, 1st edn. Cambridge University Press, CambridgeGoogle Scholar
  24. Storch H, Zwiers F (2013) Testing ensembles of climate change scenarios for “statistical significance”. Clim Chang 117(1–2):1–9CrossRefGoogle Scholar
  25. van der Linden P, Mitchell J (2009) ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project, FitzRoy Road, Exeter EX1 3PB, UK. Accessed 15 September 2013
  26. Viviroli D, Zappa M, Gurtz J, Weingartner R (2009a) An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools. Environ Model Softw 24(10):1209–1222CrossRefGoogle Scholar
  27. Viviroli D, Zappa M, Schwanbeck J, Gurtz J, Weingartner R (2009b) Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland—part I: modelling framework and calibration results. J Hydrol 377(1–2):191–207CrossRefGoogle Scholar
  28. Viviroli D, Mittelbach H, Gurtz J, Weingartner R (2009c) Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland—part II: parameter regionalisation and flood estimation results. J Hydrol 377(1–2):208–225CrossRefGoogle Scholar
  29. Wilcoxon F (1945) Individual comparisons by Ranking Methods. Biom Bull 1(6):80–83CrossRefGoogle Scholar
  30. Zappa M, Gurtz J (2003) Simulation of soil moisture and evapotranspiration in a soil profile during the 1999 MAP-Riviera Campaign. Hydrol Earth Syst Sci 7(6):903–919CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • N. Köplin
    • 1
    • 2
    Email author
  • O. Rößler
    • 1
    • 2
  • B. Schädler
    • 1
    • 2
  • R. Weingartner
    • 1
    • 2
  1. 1.Oeschger Centre for Climate Change ResearchUniversity of BernBernSwitzerland
  2. 2.Institute of Geography, Hydrology GroupUniversity of BernBernSwitzerland

Personalised recommendations