An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins
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The paper investigates climate change impacts on streamflow seasonality for a set of eleven representative large river basins covering all continents and a wide range of climatic and physiographic settings. Based on an ensemble of nine regional hydrological models driven by climate projections derived from five global circulation models under four representative concentration pathways, we analyzed the median and range of projected changes in seasonal streamflow by the end of the twenty-first century and examined the uncertainty arising from the different members of the modelling chain. Climate change impacts on the timing of seasonal streamflow were found to be small except for two basins. In many basins, we found an acceleration of the existing seasonality pattern, i.e. high-flows are projected to increase and/or low-flows are projected to decrease. In some basins the hydrologic projections indicate opposite directions of change which cancel out in the ensemble median, i.e., no robust conclusions could be drawn. In the majority of the basins, differences in projected streamflow seasonality between the low emission pathway and the high emission pathway are small with the exception of four basins. For these basins our results allow conclusions on the potential benefits (or adverse effects) of avoided GHG emissions for the seasonal streamflow regime.
This work has been conducted under the framework of ISI-MIP. The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (BMBF) with project funding reference number 01LS1201A. Responsibility for the content of this publication lies with the authors. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the respective climate modelling groups for producing and making available their model output. We would like to thank all regional-scale water sector modellers who uploaded their modelling results to the ISI-MIP server. We also acknowledge the support of the Global Runoff Data Center. We thank two anonymous reviewers for their valuable suggestions that improved the manuscript significantly.
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