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Hoang Tran, Phu Nguyen, Mohammed Ombadi, Kuolin Hsu, Soroosh Sorooshian, and Konstantinos Andreadis

Storage (CREST; Wang et al. 2011 ) as a water balance model, the kinematic wave routing ( Lighthill and Whitham 1955 ) as a routing model, and the simple mass-conserving inundation as an inundation model. The mass-conserving inundation model is a simple model that computes inundation based on stream flows and cross-sectional area and has been used widely in many studies ( Bates and De Roo 2000 ; Horritt and Bates 2002 ; Bates et al. 2003 ; Chen et al. 2009 ). Computing cross-sectional area in mass

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Zhe Li, Dawen Yang, Bing Gao, Yang Jiao, Yang Hong, and Tao Xu

processes with various precipitation inputs. This distributed modeling framework takes advantage of the geomorphologic similarities to reduce the spatial-structure complexity within a grid and to characterize the catchment topography by hillslope–stream formulation. In brief, GBHM includes the following components: a gridded discretization scheme, a subgrid parameterization scheme, a hillslope-based hydrological modeling module, and a kinematic wave flow routing module. a. Model setup Hillslope is the

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Jessica D. Lundquist, Paul J. Neiman, Brooks Martner, Allen B. White, Daniel J. Gottas, and F. Martin Ralph

. Hydrol. Processes , 12 , 1569 – 1587 . 10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L Marshall, J. S. , Langille R. C. , and Palmer W. Mc K. , 1947 : Measurements of rainfall by radar. J. Meteor. , 4 , 186 – 192 . 10.1175/1520-0469(1947)004<0186:MORBR>2.0.CO;2 Marwitz, J. D. , 1983 : The kinematics of orographic airflow during Sierra storms. J. Atmos. Sci. , 40 , 1218 – 1227 . 10.1175/1520-0469(1983)040<1218:TKOOAD>2.0.CO;2 Marwitz, J. D. , 1987 : Deep

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Tracy M. Backes, Michael L. Kaplan, Rina Schumer, and John F. Mejia

detected based on similar reanalysis data ( Dettinger et al. 2011 ; Neiman et al. 2008 ). Date and spatial matching between satellite-based ARs and our approach are consistent only when basic attributes of intensity and geometry are used to detect AR events. However, when constraining the events to low-level kinematic attributes (LLJ winds > 15 m s −1 ), as suggested by Smith et al. (2010) , Ralph and Dettinger (2011) , and Kingsmill et al. (2013) , matching drops dramatically. Not only does the

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Jie Li, Tao Tao, Zhonghe Pang, Ming Tan, Yanlong Kong, Wuhui Duan, and Yuwei Zhang

storm. Geophys. Res. Lett., 35, L21808 , doi: 10.1029/2008GL035481 . Crimp, S. , and Mason S. , 1999 : The extreme precipitation event of 11 to 16 February 1996 over South Africa . Meteor. Atmos. Phys. , 70 , 29 – 42 , doi: 10.1007/s007030050023 . D’Abreton, P. , and Tyson P. , 1996 : Three-dimensional kinematic trajectory modelling of water vapour transport over Southern Africa . Water SA , 22 , 297 – 306 . Dansgaard, W. , 1964 : Stable isotopes in precipitation . Tellus , 16

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Zuohao Cao, Jianmin Ma, and Wayne R. Rouse

), where ν (=1.46 × 10 −5 m 2 s −1 ) is the kinematic viscosity; e.g., Garratt 1994 ], We have also tested Brutsaert’s (1982) formula to see how sensitive the sensible heat flux computation is to the different roughness length parameterization, As a result, the correlation coefficients between the observed and variational-method-calculated sensible heat flux using Garratt’s (1994) and Brutsaert’s (1982) formulas are 0.66 and 0.67, respectively. Different from high-wind (>25 m s

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Tristan S. L'Ecuyer, Christian Kummerow, and Wesley Berg

reflectivity-based classification into convective and stratiform rainfall categories that account for the fundamental differences in their microphysical, thermodynamic, and kinematic properties. The Steiner et al. (1995) technique, for example, examines the intensity and spatial uniformity of the low-level reflectivity field to identify convective (high intensity, nonuniform) and stratiform (low intensity, uniform) regions. This approach has been adapted and applied to the TRMM-LBA dataset by Carey et

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Edgar L. Andreas, P. Ola G. Persson, Andrey A. Grachev, Rachel E. Jordan, Thomas W. Horst, Peter S. Guest, and Christopher W. Fairall

/ z 0 and z Q / z 0 versus the roughness Reynolds number R * = u * z 0 / ν , where ν is the kinematic viscosity of air. These plots generally follow Andreas’s (1987) theoretical model; but, again, such plots are prone to fictitious correlation. We circumvent that problem by using the flux data to compute z T / z 0 and z Q / z 0 but using our bulk flux algorithm to compute R * . We also test z 0 , z T / z 0 , and z Q / z 0 for the influence of stratification. Both z 0 and z Q

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Timothy M. Lahmers, Hoshin Gupta, Christopher L. Castro, David J. Gochis, David Yates, Aubrey Dugger, David Goodrich, and Pieter Hazenberg

functions) that must be calibrated to ensure that the original model parameters are physically consistent with catchment properties (e.g., Gupta et al. 2008 , 2009 ). For example, Pokhrel et al. (2012) used a spatial regularization approach to calibrate the NWS Hydrology Laboratory Research Distributed Hydrological Model (HL-RDHM), and Vergara et al. (2016) used spatial climate and land data to derive a priori estimates of routing parameters for the kinematic wave routing model across the CONUS to

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Jonathan J. Gourley and Baxter E. Vieux

) and momentum (B2) equations, commonly referred to as the Saint–Venant equations, are used to derive the governing equations in the Vflo model; where u is the 1D component of velocity, h is the flow depth, r is the rainfall rate, i is the soil infiltration rate, g is the acceleration due to gravity, S o is the bed slope, and S f is the friction slope. The momentum equation (B2) is simplified by making the assumptions utilized in the kinematic wave analogy. The local

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