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

regions as well as the predominant flow paths at several levels in the lower atmosphere. Section 4 provides a synthesis of the first part of the manuscript and introduces the content of the companion paper. 2. Methodology This study uses the wind fields in North American Regional Reanalysis (NARR; NCEP 2005 ; Mesinger et al. 2006 ) data to calculate kinematic backward trajectories for a database of flash flood events in order to assess the geographic origins of parcels that contribute to flash

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A. Amengual, M. Borga, G. Ravazzani, and S. Crema

of the fine spatial and temporal variability in rainfall across a watershed. Features in precipitation interact with the physical characteristics of basins, resulting in flood response closely linked to storm kinematics. Previous research has highlighted the influence of storm motion on flood response: it exerts a direct control in peak discharge and hydrograph timing ( Singh 1997 , 1998 ; de Lima and Singh 2002 ; Seo et al. 2012 ; Paschalis et al. 2014 ). In general, precipitation systems

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Joseph Bellier, Michael Scheuerer, and Thomas M. Hamill

), lifted index (LI; Galway 1956 ), and total totals index (TT; Miller 1975 ). The hypothesis is that the atmosphere instability is associated with a risk of convection and then a higher spatial variability of rainfall, hence we assume a positive relationship between SD ( i , j ) and the above indices, except SI and LI for which a negative relationship is assumed (lower values are associated with higher instability). The second group gathers kinematic predictors: storm relative helicity (SRH

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Joel R. Norris, F. Martin Ralph, Reuben Demirdjian, Forest Cannon, Byron Blomquist, Christopher W. Fairall, J. Ryan Spackman, Simone Tanelli, and Duane E. Waliser

, along with values for all the other subregions. The range of CIMC among subregions is substantial, varying from 10.0 mm h −1 for R03 to −7.4 mm h −1 for R06 and greatly exceeding the uncertainty of individual values. Nonetheless, this sizeable spatial variability largely averages out in the entire budget region since CIMC for B00 is only 0.7 mm h −1 . Fig . 11. Kinematic diagnostic profiles derived from the G-IV dropsondes composing the entire budget region (B00; black) and subregions with the

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Mengye Chen, Zhi Li, Shang Gao, Xiangyu Luo, Oliver E. J. Wing, Xinyi Shen, Jonathan J. Gourley, Randall L. Kolar, and Yang Hong

authors also indicated that the framework could simulate sediment movement downstream in the future research plan. Since 2016, the Ensemble Framework For Flash Flood Forecasting (EF5) integrated the Coupled Routing and Excess Storage (CREST) distributed hydrological model, a 1D hydraulic model, with kinematic wave channel routing, to successfully simulate multiple extreme precipitation-triggered flash flooding events in Oklahoma City and Houston at a continental-scale implementation ( Flamig et al

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Martyn P. Clark, Reza Zolfaghari, Kevin R. Green, Sean Trim, Wouter J. M. Knoben, Andrew Bennett, Bart Nijssen, Andrew Ireson, and Raymond J. Spiteri

. The Wigmosta and Lettenmaier experiments provide a test for the conditions where topography dominates subsurface flow, i.e., steep hillslopes with thin, permeable soils. Under these conditions, the slope of the water table is assumed to be parallel to the ground surface, and saturated subsurface flow can be modeled using a kinematic wave equation. Such simplifications are useful because the kinematic wave equation can be solved analytically using the method of characteristics. The Wigmosta and

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James A. Smith, Mary Lynn Baeck, Gabriele Villarini, Daniel B. Wright, and Witold Krajewski

LeMone et al. 2008 ). The Zilintinkevich C relates the roughness length for momentum z m to the roughness length for heat z h : where u * is the friction velocity, k is the von Kármán constant (0.4), η is the kinematic molecular viscosity of air (approximately 1.6 10 −5 m 2 s −1 ), and C is the Zilintinkevich coefficient. In Fig. 12 , we show the rainfall difference field over the Iowa domain for 7–8 June based on simulations with C = 0.1 (weak coupling) and C = 0.5 (strong

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James Cleverly, Chao Chen, Nicolas Boulain, Randol Villalobos-Vega, Ralph Faux, Nicole Grant, Qiang Yu, and Derek Eamus

resistance to vapor transport r aυ was determined as a function of U and C E , which is determined as a function of U , kinematic vapor flux ( ), and the specific humidity ( q ; kg kg −1 ) gradient between the surface and air ( q a − q 0 ) ( Brutsaert 1982 ; Stull 1988 ): Equation (2) is directly analogous to Ohm's law. Using profile measurements of q , r aυ was solved during each half hour across two atmospheric layers during one of three scenarios ( Fig. 1 ): 1) when r aυ across the

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Hongyi Li, Mark S. Wigmosta, Huan Wu, Maoyi Huang, Yinghai Ke, André M. Coleman, and L. Ruby Leung

of sediment is governed by the dynamics of water fluxes such as depth and velocity variation, which are not easily extractable from the IRF methods. The model introduced in this paper falls in the third category, the SVE-based methods. The SVE-based methods are rooted in the classic Saint Venant equations with simplifications at different levels, that is, kinematic wave method, diffusion wave method, etc. (e.g., Arnold et al. 1995 ; Arora and Boer 1999 ; Beighley et al. 2009 ; Lucas-Picher et

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Blandine Bianchi, Peter Jan van Leeuwen, Robin J. Hogan, and Alexis Berne

cellular communication networks. Water Resour. Res., 43, W03201 , doi:10.1029/2006WR005631 . McLaughlin, D. , 2002 : An integrated approach to hydrologic data assimilation: Interpolation, smoothing, and filtering . Adv. Water Resour. , 25 , 1275 – 1286 , doi:10.1016/S0309-1708(02)00055-6 . Meischner, P. F. , Bringi V. N. , Heimann D. , and Holler H. , 1991 : A squall line in southern Germany: Kinematics and precipitation formation as deduced by advanced polarimetric and Doppler radar

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