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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

, Hirabayashi et al. (2008) also pointed out that a statistically reliable evaluation of model performance with respect to extremes on large (global) scales is hampered by the scarcity of long-term observations. Recently, Feyen and Dankers (2009) compared the return periods of selected low-flow statistics derived from observed and simulated daily data from rivers across Europe, highlighting deficiencies of the simulations in the frost season. In an accompanying study, Dankers and Feyen (2009) reported

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

of hydrological extremes. Hannaford et al. (2011) applied the RDI methodology to produce “drought catalogues” for 23 European regions. For high flows, Parry et al. (2010) applied the same conceptual approach to propose the RFI methodology as a proxy for large-scale flood events. In addition to the existence of published historical European catalogues, the RDI and RFI methodologies have several distinct advantages for evaluating global model performance in terms of hydrological extremes: (i

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

models at the grid scale ( Troy et al. 2008 ; Lohmann et al. 2004 ). In catchment hydrology, the way models and observations are compared has recently been questioned. Gupta et al. (2008) argue for a “signature”-based evaluation of models because common metrics express model performance relative to weak benchmarks. The widely used Nash–Suttcliffe efficiency, for example, uses the mean of the observations as a benchmark. The correlation coefficient compares only relative fluctuations. Considering

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Philippe Lucas-Picher, Jens H. Christensen, Fahad Saeed, Pankaj Kumar, Shakeel Asharaf, Bodo Ahrens, Andrew J. Wiltshire, Daniela Jacob, and Stefan Hagemann

simulations must be incorporated to provide meaningful climate statistics and to identify significant model errors ( Fu et al. 2005 ). Moreover, the RCM ability to maintain the realistic large-scale circulation by the use of reanalysis LBCs allows the isolation of the regional feedbacks ( Park and Hong 2004 ). The Regional Climate Model Intercomparison Project (RMIP) seeks to improve the RCM simulations of the East Asian climate by evaluating its strengths and weaknesses in a common framework ( Fu et al

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

evaluation of a dynamic global vegetation model . J. Hydrol. , 286 , 249 – 270 . Gerten, D. , Heinke J. , Hoff H. , Biemans H. , Fader M. , and Waha K. , 2011 : Global water availability and requirements for future food production . J. Hydrometeor. , in press . Giorgi, F. , and Coppola E. , 2010 : Does the model regional bias affect the projected regional climate change? An analysis of global model projections . Climatic Change , 100 , 787 – 795 . Goosse, H. , and Fichefet T

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

–Sutcliffe efficiency criterion exceeding 0.6 and 30% over 0.7 ( Fig. 1 ). Eighty percent of the results have an absolute volume error less than 5%. The performance of the model has also been verified previously (see, e.g., Bergström and Sandberg 1983 ; Lindström et al. 1997 ; Colleuille et al. 2008 ). Colleuille et al. (2008) examined the performance of the model for several water balance components, including snow storage, soil moisture in the unsaturated zone, groundwater storage, and runoff using observed

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Aristeidis G. Koutroulis, Aggeliki-Eleni K. Vrohidou, and Ioannis K. Tsanis

performance rankings and providing predictions of similar precision and bias in their study (sparse and distant observations). Assessments of uncertainty associated with interpolation techniques available in most GIS packages suggest that kriging, IDW, Thiessen polygons, and triangulated irregular network (TIN) interpolations performed almost on the same level ( Siska and Hung 2001 ). Furthermore, regression modeling and kriging techniques require good judgment, experience, and expertise by the

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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

ease of comparison with modeling results (e.g., Haddeland et al. 2011 ). Lu et al. (2005) investigated a selection of radiation-based or temperature-based PET methods, adopted where the full range of observed meteorological variables is not available, and rated their performance against Food and Agriculture Organization (FAO) reference crop evaporation used as a standard. Recently Kingston et al. (2009) compared a variety of methods for evaluating potential evapotranspiration globally under

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