Climatic Change

, Volume 141, Issue 3, pp 419–433 | Cite as

Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins

  • Tobias Vetter
  • Julia Reinhardt
  • Martina Flörke
  • Ann van Griensven
  • Fred Hattermann
  • Shaochun Huang
  • Hagen Koch
  • Ilias G. Pechlivanidis
  • Stefan Plötner
  • Ousmane Seidou
  • Buda Su
  • R. Willem Vervoort
  • Valentina Krysanova


This paper aims to evaluate sources of uncertainty in projected hydrological changes under climate change in twelve large-scale river basins worldwide, considering the mean flow and the two runoff quantiles Q10 (high flow), and Q90 (low flow). First, changes in annual low flow, annual high flow and mean annual runoff were evaluated using simulation results from a multi-hydrological-model (nine hydrological models, HMs) and a multi-scenario approach (four Representative Concentration Pathways, RCPs, five CMIP5 General Circulation Models, GCMs). Then, three major sources of uncertainty (from GCMs, RCPs and HMs) were analyzed using the ANOVA method, which allows for decomposing variances and indicating the main sources of uncertainty along the GCM-RCP-HM model chain. Robust changes in at least one runoff quantile or the mean flow, meaning a high or moderate agreement of GCMs and HMs, were found for five river basins: the Lena, Tagus, Rhine, Ganges, and Mackenzie. The analysis of uncertainties showed that in general the largest share of uncertainty is related to GCMs, followed by RCPs, and the smallest to HMs. The hydrological models are the lowest contributors of uncertainty for Q10 and mean flow, but their share is more significant for Q90.


Emission Scenario Climate Change Impact Hydrological Model Representative Concentration Pathway Hydrological 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.



We would like to thank all regional-scale water sector modellers who uploaded their modelling results on the ISI-MIP server.

Supplementary material

10584_2016_1794_MOESM1_ESM.pdf (92 kb)
Online Resource 1 Table A1. Overview of the case studies: application of nine models in twelve river basins, and some characteristics of the basins. It was not possible to have the full coverage of all twelve basins by nine models. In four cases we used two outputs of the same model (HYMOD, HBV), applied by two modeler modelling groups for the same basins. (PDF 91 kb)
10584_2016_1794_MOESM2_ESM.pdf (102 kb)
Online Resource 2 Additional description of the applied ANOVA method (PDF 101 kb)
10584_2016_1794_MOESM3_ESM.pdf (625 kb)
Online Resource 3 Fig. A1. Overview of simulated trends for six basins: Number of HMs showing red: statistically significant negative, light gray: insignificant negative, dark gray: insignificant positive, blue: statistically significant positive trends in Q10 (upper rows), MF (middle rows) and Q90 (lower rows) for five GCMs (X axis) and 4 RCPs (left to right). Fig. A2. Overview of simulated trends for six basins: Number of HMs showing red: statistically significant negative, light gray: insignificant negative, dark gray: insignificant positive, blue: statistically significant positive trends in Q10 (upper rows), MF (middle rows) and Q90 (lower rows) for five GCMs (X axis) and 4 RCPs (left to right). Fig. A3. Temporal evolvement of total uncertainty and the share of uncertainty by GCMs, RCPs and HMs and their interaction terms for projected relative changes in three runoff quantiles: HF – Q10, MF – mean flow, LF – Q90 in six river basins: Niger (Lokoja), Darling, Lena, Upper Yangtze, Mackenzie and Ganges. (PDF 624 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Tobias Vetter
    • 1
  • Julia Reinhardt
    • 1
  • Martina Flörke
    • 2
  • Ann van Griensven
    • 3
    • 4
  • Fred Hattermann
    • 1
  • Shaochun Huang
    • 1
  • Hagen Koch
    • 1
  • Ilias G. Pechlivanidis
    • 5
  • Stefan Plötner
    • 6
  • Ousmane Seidou
    • 7
  • Buda Su
    • 8
  • R. Willem Vervoort
    • 9
  • Valentina Krysanova
    • 1
  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  2. 2.Center for Environmental Systems ResearchUniversity of KasselKasselGermany
  3. 3.Department of Hydrology and Hydraulic EngineeringVrije Universiteit BrusselBrusselsBelgium
  4. 4.UNESCO-IHE Institute for Water EducationDelftNetherlands
  5. 5.Swedish Meteorological and Hydrological InstituteNorrköpingSweden
  6. 6.Leibniz University of Hannover, Institute of Water Resources ManagementHannoverGermany
  7. 7.Department of Civil EngineeringUniversity of OttawaOttawaCanada
  8. 8.National Climate Center, China Meteorological AdministrationNanjingPeople’s Republic of China
  9. 9.Centre for Carbon, Water and FoodThe University of SydneySydneyAustralia

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