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M. F. P. Bierkens and L. P. H. van Beek

1. Introduction The ability to properly forecast river discharge at seasonal time scales is extremely beneficial to society. By searching for accurate predictions of river discharge for the coming season, one may improve reservoir management and help to ensure drinking water and food supply, hydropower generation, and river navigability ( Trigo et al. 2004 ; Wilby et al. 2004 ; Cherry et al. 2005 ). This justifies the many attempts to relate river discharge to predictable, slowly varying

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Dingchen Hou, Kenneth Mitchell, Zoltan Toth, Dag Lohmann, and Helin Wei

forecast uncertainty, we must follow an ensemble approach. While various techniques, such as an ensemble preprocessor, are used to regenerate ensemble members, direct use of the NWP ensemble members ( Pappenberger et al. 2005 , 2008 ) provides another alternative approach. It has the advantage of facilitating the coupled meteorological–hydrological modeling. Traditionally, hydrological forecast is made for individual river basins, and the predicted streamflow is valid at the outlet of the river basin

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Yongqiang Zhang, Francis H. S. Chiew, Lu Zhang, and Hongxia Li

hydrographs in the Senegal River basin. Garcia-Quijano and Barros (2005) and Gebremichael and Barros (2006) combined satellite-based estimates of LAI and fractional vegetation cover into a distributed photosynthesis–hydrological model and showed that daily variation of evaporation and photosynthesis can be captured by the interaction of biophysical and hydrological processes. Zhang and Wegehenkel (2006) added remote sensed LAI time series data to a spatially explicit water balance model and found a

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Damian J. Barrett and Luigi J. Renzullo

constrained assimilation, the analysis error will be significantly less than either the observation or background errors. The measure of the efficacy adopted in this work is the proportional constraint, 3. Study area and datasets a. Murrumbidgee River catchment Investigation focused on a 200 000 km 2 region of interest (ROI) located in southeast Australia. The study area surrounds the Murrumbidgee River catchment (∼87 000 km 2 ) and was specifically chosen to illustrate the ranges in T s

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J. M. Schuurmans and M. F. P. Bierkens

Flood Forecasting System (EFFS). Hydrol. Earth Syst. Sci. , 9 , 381 – 393 . 10.5194/hess-9-381-2005 Persson, A. , 2001 : User Guide to ECMWF forecast products. ECMWF Meteorology Bulletin M3.2., 123 pp . Roo, A. D. , and Coauthors , 2003 : Development of a European flood forecasting system. Int. J. River Basin Manage. , 1 , 49 – 59 . 10.1080/15715124.2003.9635192 Roulin, E. , 2007 : Skill and relative economic value of medium-range hydrological ensemble predictions. Hydrol

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Gabriëlle J. M. De Lannoy, Paul R. Houser, Niko E. C. Verhoest, and Valentijn R. N. Pauwels

forecasting of water levels in the River Severn catchment, United Kingdom. Water Resour. Res. , 42 , W06407 . doi:10.1029/2005WR004373 . Stauffer, D. R. , and Seaman N. L. , 1990 : Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev. , 118 , 1250 – 1277 . 10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2 van Geer, F. C. , Te Stroet C. B. , and Yangxiao Z. , 1991 : Using Kalman filtering to improve and

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Adriaan J. Teuling, Remko Uijlenhoet, Bart van den Hurk, and Sonia I. Seneviratne

reanalysis data: The Mississippi River basin. J. Climate , 17 , 2039 – 2057 . 10.1175/1520-0442(2004)017<2039:ICITWS>2.0.CO;2 Seneviratne, S. I. , Lüthi D. , Litschi M. , and Schär C. , 2006a : Land–atmosphere coupling and climate change in Europe. Nature , 443 , 205 – 209 . 10.1038/nature05095 Seneviratne, S. I. , and Coauthors , 2006b : Soil moisture memory in AGCM simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) data. J. Hydrometeor. , 7 , 1090

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