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

point, porosity, saturated hydraulic conductivity) on soil moisture and the mean water budget components under stochastic forcing. Here, potential means that the soil parameters are isolated from their original model, and their effect is evaluated using a parsimonious framework of stochastic soil moisture models. Through this methodology, we only evaluate the effect of parameters from different LSMs, not the LSMs themselves. Also, model-dependent compensating effects as a result of parameter

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

forecast—for example, the time of peak flow and total discharge—is possible only when the overall performance is of practical value. Therefore, primary effort is devoted to the statistics, or the temporal average, of the verification scores of the streamflow simulations. Nevertheless, an inspection of individual cases (i.e., simulations of a particular lead time and/or initialized at a particular day) is a necessary step of the study and helpful to the quantitative evaluation of the coupled modeling

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

errors in deep model soil layers can have a large impact on overall water balance and storage calculations. In this work, we analyze the impact of the systematic perturbation biases shown in Fig. 1 on the performance of land data assimilation using the EnKF and introduce a simple method to reduce their effect on EnKF predictions. A series of identical synthetic twin experiments are conducted to quantify the impacts of biases originating from state perturbation and to evaluate our proposed bias

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

data in each catchment were then averaged to obtain aggregate daily data series for model inputs. 4. Modeling experiments Three modeling experiments (Exp1, Exp2, and Exp3) were carried out to evaluate the benefits of using remotely sensed data in rainfall–runoff modeling ( Table 2 ). SIMHYD in Exp1 was calibrated against the observed streamflow data. SIMHYD in Exp2 was calibrated against both the observed streamflow data and E RS . SIMHYD in Exp3 was modified to use the MODIS data directly and

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Damian J. Barrett and Luigi J. Renzullo

model-observation biases T s and T b were reduced to an average of 1.6 and 1.8 K, respectively, but with the same sign. This result indicates a better performance by the water balance model in tall forests rather than in semiarid woodlands but with potentially similar sources of bias. 5. Discussion The efficacy of an observation type as a constraint on hydrological and hydrometeorological models is a function of the sensitivity of modeled observation to perturbation in the model state, the

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

) applied the maximum-likelihood method to estimate model error variance parameters in an EnKF framework, where initially only the uncertainty in the initial state or analysis was considered. In hydrology, the statistical nature of the a priori (forecast) errors has often been prescribed in synthetic studies or arbitrarily chosen in real studies. Sometimes, the filter performance is evaluated afterward by providing innovation statistics ( Reichle et al. 2002a ; Crow 2003 ; De Lannoy et al. 2007b ). In

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Vahid Naeimi, Zoltan Bartalis, and Wolfgang Wagner

. , 1999 : A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens. Environ. , 70 , 191 – 207 . 10.1016/S0034-4257(99)00036-X Wagner, W. , Scipal K. , Pathe C. , Gerten D. , Lucht W. , and Rudolf B. , 2003 : Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res. , 108 , 4611 . doi:10.1029/2003JD003663 . 10.1029/2003JD003663 Fig . 1. Geometry of the scatterometers

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