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T. Vischel, G. Quantin, T. Lebel, J. Viarre, M. Gosset, F. Cazenave, and G. Panthou

Sahelian band. Very few studies dealing with rainfall interpolation at subdaily time scales incorporate information about the rain kinematics ( Amani and Lebel 1997 ; Cheng et al. 2007 ; Spadavecchia and Williams 2009 ). Most interpolation methods process each time step independently, neglecting to take into account the valuable information contained in the time autocorrelation of rain fields (e.g. Tsanis et al. 2002 ; Haberlandt 2007 ; Tao et al. 2009 ). Several studies have shown the importance

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Christa D. Peters-Lidard and Luke H. Davis

neglecting molecular diffusion, sources and sinks, 1 and horizontal flux divergence. In addition, the horizontal x coordinate is aligned with the mean wind and the temperature gradient is assumed to be in the same direction. The simplified conservation-of-heat equation is where t is time; θ is potential temperature; u and w are the horizontal and vertical wind components, respectively; z is the vertical coordinate; and w ′ θ ′ is the kinematic heat flux. The conservation

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Augusto C. V. Getirana, Aaron Boone, Dai Yamazaki, Bertrand Decharme, Fabrice Papa, and Nelly Mognard

time. Recent studies have improved the parameterization of FRS by considering the flow routing at the subgrid scale using linear reservoirs; flow routing between grid cells based on simplified formulations of the Saint–Venant equations, such as the kinematic and diffusive wave equations; interactions between rivers and floodplains; and evaporation from open waters ( Döll et al. 2003 ; Decharme et al. 2012 ; Yamazaki et al. 2011 ). Also, recent advances in data availability, resolution, and

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John L. Williams III and Reed M. Maxwell

which model component handles the subsurface, land surface, and atmosphere. ParFlow simulates flow in the subsurface by solving the Richards equation in three spatial dimensions with integrated surface flow routing using the kinematic wave approximation of the shallow water equations. The connection to evapotranspiration (ET) is simulated through moisture stress functions that limit the actual ET against potential ET. Additional details of ParFlow’s governing equations and the connections with the

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Yanluan Lin, Brian A. Colle, and Sandra E. Yuter

deficiencies in the model bulk microphysical parameterizations (BMPs) (e.g., Colle and Mass 2000 ; Garvert et al. 2005b ; Milbrandt et al. 2008 , 2010 ). In addition to the model BMPs, errors from synoptic and mesoscale kinematic and thermodynamic fields also impact model QPF ( Richard et al. 2007 ; Roebber et al. 2008 ; Minder et al. 2008 ; Schlemmer et al. 2010 ). Low-level moisture flux is an important ingredient for orographic precipitation ( Smith 1979 ). The strong correlation between upstream

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Qiuhong Tang, Taikan Oki, Shinjiro Kanae, and Heping Hu

conductivity at saturation K s (m s −1 ), soil wetness parameter b , and porosity θ s ( Cosby et al. 1984 ). The surface overland flow is described by the one-dimensional kinematic wave model that includes the continuity equation ( Lighthill and Whitham 1955 ; Hager 1984 ): and momentum equation: where h s is the surface overland flow depth (m), q s is the overland discharge per unit width (m 2 s −1 ), t is time (s), x is the distance along the overland flow (m), i is surface

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Augusto C. V. Getirana, Aaron Boone, and Christophe Peugeot

daily spatially distributed streamflow with internal computational time steps that can be adjusted for accuracy (varying from a few minutes to several hours) as a function of the river reach length, river bed slope, and kinematic wave celerity. The spatial resolution of both ARTS and ISBA in the current study is 0.05° × 0.05°, which results in 473 grid cells for the upper Ouémé River basin. ARTS also represents the R and B time delays before reaching the river network using a linear reservoir

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Cédric H. David, David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets, and Victor Eijkhout

70 years. The Muskingum method has two parameters, k and x , respectively a time and a dimensionless parameter. Among the most noteworthy papers related to the Muskingum method, Cunge (1969) showed the Muskingum method is a first-order approximation of the kinematic and diffusive wave equation and proposed a method known as the Muskingum–Cunge method—a second-order approximation of the kinematic and diffusive wave equation—in which the Muskingum parameters are computed based on mean physical

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Yu Zhang, Seann Reed, and David Kitzmiller

runoff at each HRAP grid cell for given precipitation, snowmelt, and initial conditions ( Burnash et al. 1973 ; Burnash 1995 ; Koren et al. 2004 ). The third is a kinematic wave module for routing overland and channel flows ( Koren et al. 2004 ). The a priori SAC-SMA parameter values from physiographic information and snow model parameters commonly used in RFC operations were adopted without any calibration. These values have been shown to work well, in a relative sense, in other studies ( Reed et

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Karen I. Mohr, R. David Baker, Wei-Kuo Tao, and James S. Famiglietti

used GCE–PLACE to simulate landscape-generated and sea-breeze-generated deep convection. A computationally efficient 2D model such as GCE–PLACE is an attractive choice, supported by the results of previous studies that show that the time-mean kinematic and thermodynamic properties of a convective line are well simulated in 2D models ( Nicholls et al. 1988 ; Ferrier et al. 1996 ; Tao et al. 1996 ; Xu and Randall 1996 ; Grabowski et al. 1998 ; Lucas et al. 2000 ). The simulation of horizontal

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