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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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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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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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Jessica M. Erlingis, Jonathan J. Gourley, and Jeffrey B. Basara

varies both seasonally and regionally. The companion manuscript to this paper ( Erlingis et al. 2019 , hereafter Part I ) introduced the study domain, the flash flood database used, and the regions of interest. Kinematic trajectories were produced for 19 253 flood events and presented as they relate to the regional and seasonal mechanisms for producing heavy rainfall and flash flooding in the United States. In this manuscript, the moisture budget along the trajectories is analyzed along with the

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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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Marco Borga, Paolo Boscolo, Francesco Zanon, and Marco Sangati

distributed hydrologic model [Kinematic Local Excess Model (KLEM); Cazorzi and Dalla Fontana 1992 ]. The distributed model is based on availability of raster information of the landscape topography and of the soil and vegetation properties. In the model, the SCS-Curve Number (SCS-CN) procedure ( U.S. Department of Agriculture 1986 ) is applied on a grid-by-grid way for the spatially distributed representation of runoff generating processes, while a simple description of the drainage system response ( Da

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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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Matteo Colli, Luca G. Lanza, Roy Rasmussen, and Julie M. Thériault

constant growth rate equal to 1.2 with the first node generally located at y = 0.5 mm. Table 1. Geometric characteristics and quality factors of the different grids adopted for the RANS simulation and LES. The airflow is solved by modeling the boundary layer regions of the flow (close to the ground and the windshield–snow gauge surfaces) with specific wall functions defined according to the variables solved (the turbulent kinematic viscosity ν T , k , and Ω). This is reasonable since the problem

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Younghyun Cho and Bernard A. Engel

: The Hydrology Laboratory Research Modeling System (HL-RMS)/Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM; NWS’s operational model for DMIP1/DMIP2) employ the Sacramento Soil Moisture Accounting (SAC-SMA) lumped water balance model and the kinematic wave for hillslope–channel routing; it is a physically based conceptual model ( Koren et al. 2004 ). The Hydrologic Research Center Distributed Hydrologic Model (HRCDHM) has a method similar to HL-RMS; it is a catchment

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