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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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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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Catherine Wilcox, Claire Aly, Théo Vischel, Gérémy Panthou, Juliette Blanchet, Guillaume Quantin, and Thierry Lebel

temporal disaggregation of the cumulative event rainfall. The temporal disaggregation also includes a model of storm kinematics that consists of defining a field of hyetograph time of arrival based on prescribed storm propagation speed and direction. Fig . 1. Standard hyetograph shape from Balme et al. (2006) implemented in Stochastorm, consisting of convective and stratiform parts. 3. New developments and technical definitions The following sections detail the new features developed in Stochastorm

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Hyun Il Choi, Xin-Zhong Liang, and Praveen Kumar

R.-M. , and Simons D. B. , 1978 : Applicability of kinematic and diffusion models . J. Hydraul. Eng. , 104 , 353 – 360 . Qian, T. , Dai A. , Trenberth K. E. , and Oleson K. W. , 2006 : Simulation of global land surface conditions from 1948 to 2004: Part I: Forcing data and evaluations . J. Hydrometeor. , 7 , 953 – 975 . Richards, L. , 1931 : Capillary conduction of liquids through porous mediums . Physics , 1 , 318 – 333 . Richter, K.-G. , and Ebel M. , 2006

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Jiali Ju, Heng Dai, Chuanhao Wu, Bill X. Hu, Ming Ye, Xingyuan Chen, Dongwei Gui, Haifan Liu, and Jin Zhang

model combined with Curve Number to simulate the effect of land use change on environmental flow . J. Hydrol. , 519 , 3142 – 3152 , . 10.1016/j.jhydrol.2014.10.049 Liu , J. , X. Chen , J. Zhang , and M. Flury , 2009 : Coupling the Xinanjiang model to a kinematic flow model based on digital drainage networks for flood forecasting . Hydrol. Processes , 23 , 1337 – 1348 , . 10.1002/hyp.7255 Liu , L. , T

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Ben S. Pickering, Steven Best, David Dufton, Maryna Lukach, Darren Lyth, and Ryan R. Neely III

. Molyneux , 2006 : Results of using present weather instruments in the United Kingdom. Tech. Rep., Met Office, 13 pp. Matson , R. J. , and A. W. Huggins , 1980 : The direct measurement of the sizes, shapes and kinematics of falling hailstones . J. Atmos. Sci. , 37 , 1107 – 1125 ,<1107:TDMOTS>2.0.CO;2 . 10.1175/1520-0469(1980)037<1107:TDMOTS>2.0.CO;2 NERC, Met Office , B. S. Pickering , R. R. Neely III , and D. Harrison , 2019 : The

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Matthew D. Cann and Allen B. White

-nucleating particles . Nature , 525 , 234 – 238 , . 10.1038/nature14986 Wolfe , D. E. , and S. I. Gutman , 2000 : Developing an operational, surface-based, GPS, water vapor observing system for NOAA: Network design and results . J. Atmos. Oceanic Technol. , 17 , 426 – 440 ,<0426:DAOSBG>2.0.CO;2 . 10.1175/1520-0426(2000)017<0426:DAOSBG>2.0.CO;2 Yuter , S. E. , and R. A. Houze , 1995 : Three-dimensional kinematic and

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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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