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

. Following Blöschl (1999) , who affirms that an optimal cell size may not exist, the model element scale may in practice be dictated by practical considerations such as data availability to calibration and validation stages and the required resolution of the predictions. For the second question, a simple parameterization by means of depletion curves (DCs), which extends the point mass and energy balance calculation by using a relationship between one selected snow state variable and the snow cover

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

) Spatiotemporal distribution of precipitation To quantify precipitation we estimated rainfall and snow water equivalent (SWE) separately, combining RS-based approaches and gauge measurements. The flowchart ( Fig. 2 ) explains the determination of the rainfall, the SWE, and the total precipitation. The yearly precipitation was calculated per hydrological year (from 1 October to 30 September next year) and per season (the wet season covers 6 months between 1 October and 31 March, and the dry season covers 6

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Gift Dumedah and Jeffrey P. Walker

influence of model parametric uncertainty on multiscale snow simulation, and Dumedah et al. (2012) have assessed the time-variant properties of model parameters in streamflow estimation. Nonetheless, the contribution of model parameter convergence in relation to the estimation accuracy of DA methods has not been thoroughly examined in the DA literature. The convergence of model parameters influences the merging of observations with model predictions and thus the estimation accuracy of DA methods. In

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Junchao Shi, Massimo Menenti, and Roderik Lindenbergh

, z 0 can be estimated on the basis of the geometric characteristics of the roughness elements. According to the studies by Arya (1975) , Andreas (1987) , Oke (1987) , and Stull (2009) , the dimensions and density distribution of surface roughness elements are influential on z 0 when normal turbulence flows over melting snow and ice surfaces. Because of increasing height, surface area, and density of surface roughness, the value of z 0 increases until the ratio between the silhouette area

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Haolu Shang, Li Jia, and Massimo Menenti

, observation bias, and possible noise. The daily SSM/I and AMSR-E data gridded into the EASE-Grid ( Brodzik and Knowles 2002 ) were downloaded from the National Snow and Ice Data Center (NSIDC) ( Armstrong et al. 1998 ). The Poyang Lake area covers 10 pixels of EASE-Grid data, as indicated in Fig. 1 by the white-numbered squares. The upstream area of the Poyang Lake covers the four major tributary river systems coming from the west, southwest, southeast, and east, and indicated in Fig. 1 by the gray

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Chiara Corbari and Marco Mancini

is important, in the form of soil moisture and snow accumulation over the ground ( Castillo et al. 2003 ; Famiglietti and Wood 1994 ; Noilhan and Planton 1989 ). However, soil moisture, which is the key variable in the hydrologic water balance, is most of the time confined to an internal numerical model variable. Calibration and validation of distributed models at basin scale generally refer to external variables, which are integrated catchment model outputs, and usually depend on the

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

main components of land cover, and they have a height of 15 cm during summers and about 5 cm during winters. The Maqu station is equipped with a micrometeorological observation system and a combined soil moisture and soil temperature monitoring network. The data used in this study have been collected at the micrometeorological observation system from 20 May 2009 to 17 May 2010. The episodes with snow on the ground are excluded by using only the data records for which the observed albedo attains the

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

aggregate. The final quality check involves the elimination of data taken (i) during intensive rain events (precipitation > 10 mm h −1 ), (ii) near or below freezing conditions (temperature < 273.4 K), or (iii) when snow is present (snow water equivalent > 10 −4 kg m −2 ) based on GEOS-5 estimates of temperature, precipitation, and snow. Furthermore, only soil moisture observations with an average retrieval uncertainty (provided with the SMUDP2 product) less than 0.2 m 3 m −3 are selected. The above

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