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

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Abstract

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.

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Acknowledgments

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

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Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Tobias Vetter, Julia Reinhardt, Fred Hattermann, Shaochun Huang, Hagen Koch & Valentina Krysanova

  2. Center for Environmental Systems Research, University of Kassel, Kassel, Germany

    Martina Flörke

  3. Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium

    Ann van Griensven

  4. UNESCO-IHE Institute for Water Education, Delft, Netherlands

    Ann van Griensven

  5. Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

    Ilias G. Pechlivanidis

  6. Leibniz University of Hannover, Institute of Water Resources Management, Hannover, Germany

    Stefan Plötner

  7. Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada

    Ousmane Seidou

  8. National Climate Center, China Meteorological Administration, Nanjing, People’s Republic of China

    Buda Su

  9. Centre for Carbon, Water and Food, The University of Sydney, Sydney, Australia

    R. Willem Vervoort

Authors
  1. Tobias Vetter
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  2. Julia Reinhardt
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  3. Martina Flörke
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  5. Fred Hattermann
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  6. Shaochun Huang
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  7. Hagen Koch
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  8. Ilias G. Pechlivanidis
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  9. Stefan Plötner
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  11. Buda Su
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  12. R. Willem Vervoort
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  13. Valentina Krysanova
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Corresponding author

Correspondence to Tobias Vetter.

Additional information

This article is part of a Special Issue on “ Hydrological Model Intercomparison for Climate Impact Assessment” edited by Valentina Krysanova and Fred Hattermann.

Tobias Vetter and Julia Reinhardt contributed equally to this paper.

Electronic supplementary material

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)

Online Resource 2

Additional description of the applied ANOVA method (PDF 101 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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Vetter, T., Reinhardt, J., Flörke, M. et al. Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Climatic Change 141, 419–433 (2017). https://doi.org/10.1007/s10584-016-1794-y

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