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

1. Introduction Improving the accuracy of runoff predictions in ungauged catchments is one of most challenging tasks in hydrology ( Franks et al. 2005 ; Goswami et al. 2007 ; Sivapalan et al. 2003 ). Parameter regionalization in lumped rainfall–runoff models is a commonly used method to transfer optimized parameter values to target ungauged catchments. Various regionalization methods have been developed, such as nearest neighbor, kriging, site similarity, and regression methods ( Kay et al

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

data assimilation study, Reichle and Koster (2003) demonstrated that taking horizontal model error correlations into account in the a priori error covariance matrix for a 3D ensemble Kalman filter (EnKF) improved the estimation accuracy for soil moisture, even in a regional-scale application. Other existing approaches to enforce lateral information flow in an assimilation scheme with a 1D (column) model are a priori interpolation of the observations to locations where they are desired to affect

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Damian J. Barrett and Luigi J. Renzullo

observation operator (i.e., known as the differential of H ) derived from the TLM. A measure of the accuracy of the analysis (i.e., the updated state variable) is provided by the analysis error covariance matrix 𝗔, Matrix 𝗛 contains information on the effects of perturbations of the state variable on the modeled observation. Here we denote perturbations on the soil moistures, θ z and θ s 1 , collectively as δθ , and the resulting variation in modeled temperatures, T s and T b , as δT . We

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

the highest skill. This means that the effects on skill by initial conditions and on skill by NAO-based forecasting are orthogonal—that is, the combined effect is a weighted sum of the two individual effects. Also, the regions with the largest skill resulting from using NAO-based seasonal predictions coincide with the regions where river discharge is sensitive to the NAO phase (large positive or large negative z scores): Spain, the region around the Black Sea (including the lower Danube), and

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

variable, which can be generated by river routing models from runoff, is not available. River routing experiments are carried out at NCEP within the North America Land Data Assimilation System (NLDAS) project ( Lohmann et al. 2004 ). The NLDAS project ( Mitchell et al. 2004 ) runs four land surface models in analysis mode over the continental United States (CONUS) domain with an 1/8° grid separation, by taking meteorological input from the regional reanalysis, which includes estimated real hourly

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