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

1. Introduction Insight into the spatial distribution of soil moisture within a catchment is of great importance to many groups, for example, farmers and water management boards. Accurate short- to medium-range prediction of spatially distributed soil moisture is helpful for optimizing irrigation gifts, forecasting hydrological drought, and assessing catchment wetness for flood control. Because rainfall is one of the most important input variables in hydrological models, the accuracy of the

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

1. Introduction Flooding and drought are the most frequent natural hazards, and water resource management is one of the most challenging problems the world is facing. Therefore, hydrological forecast, especially streamflow forecast, is of great interest, and it is a major application of numerical weather prediction (NWP) output. NWP forecasts of precipitation and temperature can be incorporated into a flood warning system, and the forecast lead time can be significantly increased (e

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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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Dongryeol Ryu, Wade T. Crow, Xiwu Zhan, and Thomas J. Jackson

sensed surface soil moisture retrievals (e.g., Margulis et al. 2002 ; Crow and Wood 2003 ; Reichle and Koster 2005 ; Reichle et al. 2007 ) into land surface models (LSMs) from local to global scales. In the EnKF approach, an ensemble of model states is generated by adding noise to state variables, model parameters, and/or forcing variables. The uncertainty of land surface model forecasts is then represented by the spread of these stochastically generated ensemble states. A Gaussian random number

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

the sum of the predictability and model error covariances. For optimization, several adaptive filters have been developed to estimate the model error covariances, mostly involving some whitening of the innovation (observation minus forecast) sequence ( Kailath 1968 ; Mehra 1970 ; Maybeck 1982 ) or matching (elements of) the zero lag innovation covariance matrix to the theoretically optimal values ( Jazwinski 1969 ; Myers and Tapley 1976 ; Maybeck 1982 ; Mehra 1972 ; Dee 1995 ; Dee and da

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Damian J. Barrett and Luigi J. Renzullo

1. Introduction Increasingly, methods of data assimilation are being applied to both hydrological and hydrometeorological problems driven by prospects of better characterization of initial conditions and improved forecasting skill ( Mecikalski et al. 1999 ; Reichle et al. 2001 ; Crosson et al. 2002 ; Reichle et al. 2002 ; Heathman et al. 2003 ; Merlin et al. 2006 ; Pan et al. 2008 ; Wang and Cai 2008 ; Barrett et al. 2008 ). The benefits afforded by the application of data assimilation

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

numerically. For any parameter ∏, the relative sensitivity σ of 〈ET〉 to ∏ can be approximated by where Δ〈ET〉 is the range in 〈ET〉, resulting from a small parameter perturbation Δ∏ (here 1%). h. ELDAS Soil parameters 1) TESSEL and HTESSEL Tiled ECMWF Scheme for Surface Exchanges over Land has been developed at the European Centre for Medium Range Weather Forecasts (ECMWF). In the original version of TESSEL, only one soil type exists. The physical properties of this soil ( Table 2 ) were chosen

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

: Principles of Environmental Physics . Butterworth-Heinemann, 291 pp . Nash, J. E. , and Sutcliffe J. V. , 1970 : River flow forecasting using conceptual models. Part I—A discussion of principles. J. Hydrol. , 10 , 282 – 290 . 10.1016/0022-1694(70)90255-6 Oudin, L. , Andréassian V. C. , Perrin C. , Michel C. , and Le Moine N. , 2008 : Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments

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