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

Vetter, T., Reinhardt, J., Flörke, M., Griensven, A. v., Hattermann, F. F., Huang, S., Koch, H., Pechlivanidis, I. G., Plötner, S., Seidou, O., Su, B., Vervoort, R. W., Krysanova, V. (2017): Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. - Climatic Change, 141, 3, 419-433.
https://doi.org/10.1007/s10584-016-1794-y

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Vetter, Tobias1, Author              
Reinhardt, Julia1, Author              
Flörke, M.2, Author
Griensven, A. van2, Author
Hattermann, Fred Fokko1, Author              
Huang, Shaochun1, Author              
Koch, Hagen1, Author              
Pechlivanidis, I. G.2, Author
Plötner, S.2, Author
Seidou, O.2, Author
Su, B.2, Author
Vervoort, R. W.2, Author
Krysanova, Valentina1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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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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 Dates: 2017
 Publication Status: Finally published
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s10584-016-1794-y
PIKDOMAIN: Climate Impacts & Vulnerabilities - Research Domain II
eDoc: 7305
Research topic keyword: Climate impacts
Research topic keyword: Freshwater
Model / method: Model Intercomparison
Model / method: SWIM
Regional keyword: Asia
Regional keyword: Global
Organisational keyword: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
 Degree: -

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Title: Climatic Change
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 141 (3) Sequence Number: - Start / End Page: 419 - 433 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals80