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 Griensven, Fred Hattermann, Shaochun Huang, Hagen Koch, Ilias G. Pechlivanidis, Stefan Plötner, Ousmane Seidou, Buda Su, R. Willem Vervoort and Valentina Krysanova
Additional contact information
Tobias Vetter: Potsdam Institute for Climate Impact Research
Julia Reinhardt: Potsdam Institute for Climate Impact Research
Martina Flörke: University of Kassel
Ann Griensven: Vrije Universiteit Brussel
Fred Hattermann: Potsdam Institute for Climate Impact Research
Shaochun Huang: Potsdam Institute for Climate Impact Research
Hagen Koch: Potsdam Institute for Climate Impact Research
Ilias G. Pechlivanidis: Swedish Meteorological and Hydrological Institute
Stefan Plötner: Leibniz University of Hannover, Institute of Water Resources Management
Ousmane Seidou: University of Ottawa
Buda Su: National Climate Center, China Meteorological Administration
R. Willem Vervoort: The University of Sydney
Valentina Krysanova: Potsdam Institute for Climate Impact Research

Climatic Change, 2017, vol. 141, issue 3, No 5, 419-433

Abstract: 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.

Date: 2017
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DOI: 10.1007/s10584-016-1794-y

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