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Rafael Pimentel, Javier Herrero, Yijian Zeng, Zhongbo Su, and María J. Polo

evolution is made by using simple empirical relationships between the snowmelt flux and selected meteorological variables ( Kustas et al. 1994 ). However, in these areas, the marked annual, seasonal, and even weekly variability of temperature, wind, and rainfall make this a difficult approach to apply in practice, and energy and mass balance equations are usually needed to capture these highly variable conditions ( Anderson 1976 ). Many physically based point models for the mass and energy balance in

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Chiara Corbari and Marco Mancini

1. Introduction Calibration and validation of continuous distributed energy water balance models is a challenging task in hydrology and at the same time a complex issue owing to the difficulties related to the definition of which variables are representative of the single process and how reliable they are ( Beven and Binley 1992 ; Refsgaard 1997 ; Rabuffetti et al. 2008 ; Brath et al. 2004 ). In flood and water balance simulations, the exact representation of the surface boundary conditions

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Gabriëlle J. M. De Lannoy, Rolf H. Reichle, and Valentijn R. N. Pauwels

1. Introduction Assimilating low-frequency (1–10 GHz) passive microwave observations into land surface models is expected to improve estimates of land surface conditions and, hence, weather and climate predictions. Global observations of brightness temperatures (Tb) are available from the (late) Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), the Soil Moisture Ocean Salinity (SMOS; Kerr et al. 2010 ) mission, and Aquarius ( Le Vine et al. 2007 ). Soil moisture has a

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Martijn J. Booij, Arjen Y. Hoekstra, and Jun Wen

soil NDVI) and NDVI max is maximum NDVI (or full canopy NDVI). The values of NDVI min and NDVI max are specified as 0.8 and 0.1 respectively. A detailed description of the NDVI products and data processing can be found in Chen et al. (2013) . Application of Noah in a default mode accommodates four soil layers with thicknesses of 0.1, 0.3, 0.6, and 1.0 m, respectively. The initial conditions of surface temperature and temperature in each layer are specified based on the measurements. The model

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Mustafa Gokmen, Zoltan Vekerdy, Maciek W. Lubczynski, Joris Timmermans, Okke Batelaan, and Wouter Verhoef

detect the groundwater conditions directly are limited ( Green et al. 2011 ). One of the major exceptions to this is the satellite-based observations of Earth's gravity field: changes in total surface and subsurface storage can be derived using gravity anomaly measurements with the Gravity Recovery and Climate Experiment (GRACE) satellites ( Swenson and Wahr 2002 ). However, with spatial resolution of 400–500 km this technique can provide change of groundwater storage over regions of about 150 000 km

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Haolu Shang, Li Jia, and Massimo Menenti

water saturation condition is represented by the sum of its subregion conditions. The fractional area f i and the water saturation condition of each subpixel W i / W sat, i in Eq. (3) are transformable to each other, because both can be taken as the weight factor of the other. Thus, in Eq. (3) , the fractional area of WSS and standing water has the same definition as the regional water saturation condition W r,s / W sat . According to De Ridder (2000) , the regional water saturation

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