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Yuning Shi, Kenneth J. Davis, Christopher J. Duffy, and Xuan Yu

many studies (e.g., Ibbitt 1970 ; Johnston and Pilgrim 1976 ; Pickup 1977 ; Gupta and Sorooshian 1985 ; Duan et al. 1992 ; Sorooshian et al. 1993 ; Franchini 1996 ; Vrugt et al. 2003 ; Wagener et al. 2003 ; Kollat and Reed 2006 ; Xie and Zhang 2010 ). Most of the previous studies focus on the calibration of model discharge, and sometimes water table depth, but tend to neglect other observations. However, “an acceptable model prediction might be achieved in many different ways, i

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Nicola Montaldo, Matteo Curreli, Roberto Corona, Andrea Saba, and John D. Albertson

the Sivapalan et al. (1987) approach as (4) Σ = { ⁡ [ 2 k s ⁡ ( θ s − θ ) 2 ⁡ ( − ψ b θ s − θ r ) ] ⁡ [ 1 ⁡ ( 2 b + 3 ) + 1 / 2 b + θ s − θ r θ s − θ ] } 1 / 2 , where ψ b is the air entry suction head. 2) Estimating the k s parameter for the infiltration model Using the observations of discharge and soil moisture from each experiment, the parameters of the infiltration model were calibrated to minimize the errors between the modeled and observed discharges. Indeed, based on the difference

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Ryan Gonzalez and Christian D. Kummerow

relationships of snow depth and physical predictor variables ( Balk and Elder 2000 ; Molotch et al. 2005 ; López-Moreno and Nogués-Bravo 2006 ). Fassnacht et al. (2003) and Dawson et al. (2016) used linear regression and piecewise linear regression, respectively, to distribute Snowpack Telemetry (SNOTEL) snow depth measurements across different elevation ranges. These methods have shown success in producing gridded estimates of snow depth that are consistent with observations; however, they are not

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Xuejian Cao, Youcun Qi, and Guangheng Ni

hydrographs and the distribution of the urban inundation. For a developed urban area, the building area generally accounts for a large proportion. Yu et al. (2010) evaluated the building density in downtown Houston using airborne lidar data and an object-based method a decade ago. Results show that more than 40% of hundreds of land lots have a value of building coverage ratio (BCR) larger than 0.5, i.e., over 50% of ground space is occupied by building structures. More importantly, field observation

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Jorge Arevalo, Josh Welty, Yun Fan, and Xubin Zeng

-to-pixel interpolation ( Broxton et al. 2016b ), comparison against independent snow cover extent and airborne lidar measurements ( Dawson et al. 2018 ), and an evaluation against Gamma SWE ( Cho et al. 2020 ) performed by an independent research group. Hence, the UA SWE dataset will be used as the ground truth for SWE. Furthermore, data from the SNOTEL network for more than 650 stations are also used for evaluation of LB SWE and for reference comparison against the North American Land Data Assimilation System

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Bailing Li, Matthew Rodell, Christa Peters-Lidard, Jessica Erlingis, Sujay Kumar, and David Mocko

correlations between Noah-MP and CLSM simulated groundwater storage and in situ observations at regional scales in the central and eastern United States (mostly R 2 > 0.6; Li and Rodell 2015 ; Xia et al. 2017 ) and globally for CLSM (mostly R 2 > 0.5; Li et al. 2019b ). The same studies reported low correlations at point scales, due to the strong influence of subsurface properties and the limitation of coarse-scale forcing data. Since Noah-MP and CLSM do not explicitly compute fluxes between the

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Xuejian Cao, Guangheng Ni, Youcun Qi, and Bo Liu

://doi.org/10.1016/j.jclepro.2019.03.028 . 10.1016/j.jclepro.2019.03.028 Lyu , H. , G. Ni , X. Cao , Y. Ma , and F. Tian , 2018 : Effect of temporal resolution of rainfall on simulation of urban flood processes . Water , 10 , 880 , https://doi.org/10.3390/w10070880 . 10.3390/w10070880 Ozdemir , H. , C. C. Sampson , G. A. M. de Almeida , and P. D. Bates , 2013 : Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data . Hydrol. Earth Syst

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Don Cline, Simon Yueh, Bruce Chapman, Boba Stankov, Al Gasiewski, Dallas Masters, Kelly Elder, Richard Kelly, Thomas H. Painter, Steve Miller, Steve Katzberg, and Larry Mahrt

approximately 1280 m above ground level (AGL) via airborne lidar, normalized to ground controls and processed to remove noise and redundancies ( Corbley 2003 ). The elevation observations have approximately 1.5-m horizontal spacing and approximately 0.05-m vertical tolerances. The pixel size of the orthophotographs is 0.15 m. The snow-free and snow-covered elevation data with the orthoimagery provide detailed information about the distribution of snow depth in relation to vegetation distribution and height

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Yan Zhang, James A. Smith, Alexandros A. Ntelekos, Mary Lynn Baeck, Witold F. Krajewski, and Fred Moshary

the frontal zone are based on high-resolution rainfall fields derived using the Hydro-Next Generation Weather Radar (NEXRAD) system ( Krajewski et al. 2007 ). Volume scan radar reflectivity observations and cloud-to-ground (CG) lightning observations from the National Lightning Detection Network (NLDN) are used to examine convective evolution of organized thunderstorm systems embedded in the frontal zone. Disdrometer and lidar observations are used to examine microphysical processes associated

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John M. Forsythe, Jason B. Dodson, Philip T. Partain, Stanley Q. Kidder, and Thomas H. Vonder Haar

predict TPW. Rossow et al. (2005) and Rossow and Zhang (2010) used optical thickness and cloud-top pressure from the International Satellite Cloud Climatology Project (ISCCP) to estimate cloud vertical distributions and structure, and compared the predictions to CloudSat / Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) mission cloud vertical occurrence profiles. No information on water vapor was used. Rossow and Zhang (2010) point out the important distinction

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