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Kalimur Rahman and Firat Y. Testik

e 2 = C d   U t 2 D 2 υ a 2 = 2 ⁡ ( ρ d − ρ a ) V g D 2 ρ a A υ a 2 . Here, υ a is the kinematic viscosity of air. Once a unique relationship between X and Re is established, Re can be computed using the value of X , which can be calculated from the known physical properties of the drop and ambient conditions using Eq. (4) . The terminal fall speed of a drop can then be estimated using the following equation without use of the drag coefficient information: (5) U t = μ a Re ρ a D . Beard

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Kian Abbasnezhadi, Alain N. Rousseau, Étienne Foulon, and Stéphane Savary

would be time consuming. The parameters associated with the channel flow process, computed using the kinematic wave equation, were also not found to be sensitive. Previous VARS applications performed by Foulon et al. (2019) in two basins in southern Quebec yielded different results for the vertical water budget parameters. Z1 was shown to be the least sensitive soil layer thickness, while Z2 and Z3 were the second most and the most sensitive parameters, respectively. Also, the recession

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Vincent Häfliger, Eric Martin, Aaron Boone, Florence Habets, Cédric H. David, Pierre-A. Garambois, Hélène Roux, Sophie Ricci, Lucie Berthon, Anthony Thévenin, and Sylvain Biancamaria

computing cost. Even if the Muskingum method can lead to good-quality results, as shown by David et al. (2011a , b) , in our case it has severe limitations. The flow velocity is constant whatever the regime, levels are not simulated, and backwater and floodplain storage effects are not taken into account. River models, which have been further improved to use more detailed routing schemes based on the kinematic or diffusive wave, develop floodplain inundation schemes ( Getirana et al. 2012 ; Yamazaki

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Malik Rizwan Asghar, Tomoki Ushiyama, Muhammad Riaz, and Mamoru Miyamoto

also utilized the GSMaP satellite observed rainfall to run a hydrological model beforehand, after their biases were corrected based on ground rain gauges. For the hydrological component, we applied two types of models. For the upper-reach mountainous area, the Integrated Flood Analysis System (IFAS) ( Sugiura et al. 2010 ), a kinematic wave approximation model, was applied to reduce computational time. For the lower-reach flat area, the rainfall–runoff–inundation (RRI) model ( Sayama et al. 2012

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Nikolaos S. Bartsotas, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Stavros Solomos, and George Kallos

response of the Sesia River basin is a simple spatially distributed hydrologic model [Kinematic Local Excess Model (KLEM)], which has been previously used in several flash flood studies in the same area ( Sangati et al. 2009 ) as well as other similar mountainous regions ( Borga et al. 2007 ; Zoccatelli et al. 2011 ). The distributed model is based on availability of spatially distributed information on land surface properties (topography, soil type, and land use/cover). Runoff generation within the

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J.-D. Creutin, E. Leblois, and J.-M. Lepioufle

precipitation separating de facto two scales of kinematics. The “large scale” movement of precipitation systems is often clearly visible on radar animations and used as such for short-term precipitation forecasting (see, e.g., Austin and Bellon 1974 ; Bowler et al. 2004 ). It can be mathematically formulated between instants t 1 and t 2 as , where is the space vector maximizing the ACF . This large-scale movement allows one to distinguish the rainfall variability seen from a coordinate system

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Long Yang, James Smith, Mary Lynn Baeck, Efrat Morin, and David C. Goodrich

/semiarid watersheds? We examine these questions based on empirical analyses of observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar at Phoenix, high-resolution (15 min) rain gauge observations, and direct/indirect stream gauging records. Storm-tracking algorithms will be used to characterize the Lagrangian properties of flood-producing storms. We further examine the flood response of the 19 August 2014 storm based on hydrological modeling analyses using the Kinematic Runoff and Erosion

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William P. Kustas, John H. Prueger, J. Ian MacPherson, Mengistu Wolde, and Fuqin Li

estimate with Eq. (3) is certainly closer to aircraft-based value resulting in a ratio of 〈 z o 〉 EQ5 /〈 z o 〉 EQ3 ≈ 1.5. The fact that aggregation of the roughness values is less than the aircraft-based estimates suggests that the interactions of neighboring roughness areas play a significant role in the effective roughness for this landscape. Interspacing of the corn- and soybean fields as well as roads/right of ways are likely to induce additional kinematic stress via additional form drag and

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Ju-Mee Ryoo, Sen Chiao, J. Ryan Spackman, Laura T. Iraci, F. Martin Ralph, Andrew Martin, Randall M. Dole, Josette E. Marrero, Emma L. Yates, T. Paul Bui, Jonathan M. Dean-Day, and Cecilia S. Chang

). Fig . 6. Time series of (red) observed and (blue) modeled (a) wind speed at BBY, (b) precipitation over BBY (coastal region), (c) precipitation over CZD (mountain region), and (d) rain ratio (CZD/BBY) from 1800 UTC 10 Mar to 0000 UTC 12 Mar 2016. Observed wind data is only available over BBY from the wind profiler. 4. Characteristics of TTAs simulated from WRF-ARW a. Kinematic characteristic of TTAs To further understand the detailed kinematic structure and characteristics of TTAs in conjunction

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F. C. Sperna Weiland, L. P. H. van Beek, J. C. J. Kwadijk, and M. F. P. Bierkens

network. The drainage network is based on the 30′ global drainage direction map (DDM30) dataset and has a vertical resolution of 0.5° ( Döll and Lehner 2002 ). 2) Routing of total runoff (MRRO) Within this method, cell-specific total runoff (MRRO) is accumulated for each individual time step and, as an extension of the above method, routed along a drainage network using the kinematic wave equation. The drainage network is based on DDM30 and has a vertical resolution of 0.5° ( Döll and Lehner 2002

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