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Pete Falloon, Richard Betts, Andrew Wiltshire, Rutger Dankers, Camilla Mathison, Doug McNeall, Paul Bates, and Mark Trigg

1. Introduction River flow is a useful indicator of freshwater availability, and can thus be used to evaluate likely impacts of climate change on water resources and flooding. There have been a number of studies of changes in river flow at the global scale (e.g., Arora and Boer 1999 ; Arnell 1999b , 2003 ; Hagemann and Dumenil 1998 ; Hirabayashi et al. 2008 ; Milly et al. 2005 ; Nijssen et al. 2001a , b ) using either stand-alone hydrological models driven by climate data output from

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Richard Harding, Martin Best, Eleanor Blyth, Stefan Hagemann, Pavel Kabat, Lena M. Tallaksen, Tanya Warnaars, David Wiberg, Graham P. Weedon, Henny van Lanen, Fulco Ludwig, and Ingjerd Haddeland

equal or even greater impact on water resources. Globally, freshwater resources far exceed human requirements. However, by the end of the twenty-first century these requirements will begin to approach total available water. Regionally, water demands—for agriculture and domestic/industrial use—already exceed supply ( Vörösmarty et al. 2000 ). This is likely to be exacerbated with increasing population and society’s changing water demands, a situation exacerbated by the need to maintain river flows

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Manuel Punzet, Frank Voß, Anja Voß, Ellen Kynast, and Ilona Bärlund

stepwise approach to gain one single regression equation for use in impact analysis of climate change on stream water temperatures and related in-stream first-order decay rates: calculation of a global standard regression model, testing of various formulations for different climate zones, testing of seasonal hysteresis effects on a global scale, and validation with individual rivers in different climate zones. (i) Global standard regression model The nonlinear regression model [Eq. (1 )] was applied

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Stefan Hagemann, Cui Chen, Jan O. Haerter, Jens Heinke, Dieter Gerten, and Claudio Piani

simulations, several catchments were selected ( Fig. 4 ), for which river discharge data have been compiled by Dümenil Gates et al. (2000) . The catchments comprise the following regions representing different climate regimes: the Amazon, Amur, Arctic Ocean represented by its six largest rivers (Jenisei, Kolyma, Lena, Mackenzie, Northern Dvina, and Ob), Baltic Sea catchment (land only), Congo, Danube, Ganges/Brahmaputra, Mississippi, Murray, Nile, Parana (La Plata), and Yangtze Kiang. Fig . 4. Selected

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Christel Prudhomme, Simon Parry, Jamie Hannaford, Douglas B. Clark, Stefan Hagemann, and Frank Voss

variables such as soil moisture (e.g., Sheffield and Wood 2007 , 2008a ; Sheffield et al. 2009 ). However, while some of the most pressing direct impacts of droughts and floods are primarily related to runoff and river flows, comparatively little effort has focused on examining runoff extremes in large-scale models. A number of studies have examined runoff from global models at continental scales, but these studies have tended to focus on mean runoff (e.g., Milly et al. 2005 ; Gedney et al. 2006

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Ingjerd Haddeland, Douglas B. Clark, Wietse Franssen, Fulco Ludwig, Frank Voß, Nigel W. Arnell, Nathalie Bertrand, Martin Best, Sonja Folwell, Dieter Gerten, Sandra Gomes, Simon N. Gosling, Stefan Hagemann, Naota Hanasaki, Richard Harding, Jens Heinke, Pavel Kabat, Sujan Koirala, Taikan Oki, Jan Polcher, Tobias Stacke, Pedro Viterbo, Graham P. Weedon, and Pat Yeh

aim is to improve our understanding of current and future water availability and water stress at the global scale, with an emphasis on the available water resources of major river systems at the subannual time scale. Water demands involve strong seasonal variations; hence, both annual water volumes and seasonal timing are important factors. Through integrated model intercomparison and evaluation, participating models will improve the parameterization of human interactions with the global

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Lukas Gudmundsson, Lena M. Tallaksen, Kerstin Stahl, Douglas B. Clark, Egon Dumont, Stefan Hagemann, Nathalie Bertrand, Dieter Gerten, Jens Heinke, Naota Hanasaki, Frank Voss, and Sujan Koirala

global and continental scales will in the following be referred to as large-scale hydrological models. Various efforts have been made to evaluate large-scale hydrological models, including macroscale studies that compare observed and modeled continental river discharge (e.g., Balsamo et al. 2009 ; Decharme and Douville 2007 ; Gerten et al. 2004 ; Hagemann et al. 2009 ), as well as studies with relatively detailed spatial and temporal resolution on continental and global scales (e.g., Döll et al

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Wai Kwok Wong, Stein Beldring, Torill Engen-Skaugen, Ingjerd Haddeland, and Hege Hisdal

relevance to the water industry when investigating environmental demands on river systems. Climate change has the potential to alter hydrological conditions and these changes could have a large adverse effect on the availability of the water resources. Few studies have examined climate change impacts on future droughts. A study by Calanca (2007) stated that the frequency of soil moisture droughts will increase in summer in the Alpine region in Europe. Blenkinsop and Fowler (2007) used six regional

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Kerstin Stahl, Lena M. Tallaksen, Lukas Gudmundsson, and Jens H. Christensen

1. Introduction Large-scale gridded models, including global (general circulation) and regional climate models and large-scale hydrological models, are employed for a variety of purposes in hydrology and related disciplines. They provide spatial simulations of hydrological variables such as soil moisture, runoff, and river discharge for historical records and can be used to simulate the response of the hydrological cycle to future global change, that is, climate scenarios and human impacts

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G. P. Weedon, S. Gomes, P. Viterbo, W. J. Shuttleworth, E. Blyth, H. Österle, J. C. Adam, N. Bellouin, O. Boucher, and M. Best

given to regional variations in the selected large river basins shown in Fig. 1 . Fig . 1. Location map for the FLUXNET sites used in Figs. 2 – 4 (indicated by plus signs) and for the large river basins considered in Figs. 7 – 9 (indicated in black). 2. The WATCH Forcing Data The WFD consist of subdaily, regularly (latitude–longitude) gridded, half-degree resolution, meteorological forcing data. The variables included are (i) wind speed at 10 m, (ii) air temperature at 2 m, (iii) surface

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