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Sheng Wang, Suxia Liu, Xingguo Mo, Bin Peng, Jianxiu Qiu, Mingxin Li, Changming Liu, Zhonggen Wang, and Peter Bauer-Gottwein

include 1) a multilayer canopy radioactive transfer module; 2) a two-source soil–canopy energy balance module; 3) a multiple-layer soil water and energy transfer module; 4) a modified variable infiltration capacity scheme for runoff generation; 5) the degree-day factor method for snow and ice melt computation; and 6) the kinematic wave scheme for streamflow routing. The VIP model has been applied and validated extensively in basins across China ( Mo and Liu 2001 ; Mo et al. 2004 , 2014 ; Liu et al

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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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Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Marco Borga

( Ivanov et al. 2004a , b ; Vivoni et al. 2007 ). The tRIBS model is a distributed physics-based model that explicitly accounts for the spatial variability of land surface descriptors (terrain, soil, and vegetation), soil moisture, and atmospheric forcing. Infiltration is simulated in a sloped heterogeneous and anisotropic soil based on a kinematic approximation for unsaturated flow ( Cabral et al. 1992 ; Garrote and Bras 1995 ). An adaptive multiple resolution approach based on TINs ( Vivoni et al

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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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Timothy J. Lang, Steven A. Rutledge, and Robert Cifelli

.1175/1520-0493(2000)128<2687:TRBPAL>2.0.CO;2 Cetrone, J. , and Houze R. A. , 2006 : Characteristics of tropical convection over the ocean near Kwajalein. Mon. Wea. Rev. , 134 , 834 – 853 . 10.1175/MWR3075.1 Cifelli, R. , Petersen W. A. , Carey L. D. , Rutledge S. A. , and da Silva Dias M. A. F. , 2002 : Radar observations of kinematic, microphysical, and precipitation characteristics of two MCSs in TRMM LBA. J. Geophys. Res. , 107 , 8077 . doi:10.1029/2000JD000264 . 10.1029/2000JD000264 Cifelli, R

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Jonathan J. Gourley, Scott E. Giangrande, Yang Hong, Zachary L. Flamig, Terry Schuur, and Jasper A. Vrugt

-RDHM The model concepts used in this study originate from the Sacramento model ( Burnash et al. 1973 ). This model was subdivided into grid cells having 4.76-km resolution, in accordance with the NWS Hydrologic Rainfall Analysis Project (HRAP) grid. Each grid cell has a water balance component as well as kinematic overland and channel routing components ( Koren et al. 2004 ). The water balance component of the model, referred to as the Sacramento Soil Moisture Accounting model (SAC-SMA), considers

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Luis Gimeno, Raquel Nieto, Ricardo M. Trigo, Sergio M. Vicente-Serrano, and Juan Ignacio López-Moreno

. 2006 ), numerical water vapor tracers ( Joussame et al. 1984 ; Koster et al. 1986 ), and algorithms that use quasi-isentropic back trajectories in combination with model-derived surface fluxes to determine evaporation sources along back trajectories ( Dirmeyer and Brubaker 1999 ; Reale et al. 2001 ; Dirmeyer and Brubaker 2007 ). Most of the earlier-mentioned Lagrangian studies, however, have been limited by methodological (no kinematic trajectories) and conceptual (e.g., assumptions about how

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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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William Amponsah, Lorenzo Marchi, Davide Zoccatelli, Giorgio Boni, Marco Cavalli, Francesco Comiti, Stefano Crema, Ana Lucía, Francesco Marra, and Marco Borga

) Spatially distributed rainfall–runoff model A distributed hydrologic model is used to examine hydrologic response associated with space–time radar rainfall variability and to check consistency with postflood indirect peak flow estimates. The Kinematic Local Excess Model (KLEM; Marchi et al. 2010 ) combines a grid-based runoff-generation model and a network-based hillslope and channel transport model. Runoff generation is simulated by applying the Soil Conservation Service Curve Number (SCS-CN) approach

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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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