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Roland J. Viger, Lauren E. Hay, Steven L. Markstrom, John W. Jones, and Gary R. Buell

three different sets of inputs that describe 1) changes in urbanization as projected by a land-cover change model, 2) changes in climate as projected by general circulation models (GCMs), and 3) the combination of changes in urbanization and climate. The hydrologic effect of each set of inputs was determined by comparing the resultant simulations to those created by using the same hydrologic model run with inputs describing current land-cover and climate conditions. The basin used in this study, the

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Younghyun Cho and Bernard A. Engel

( Ogden et al. 2003 ), which is a physically based, distributed parameter, structured grid, hydrologic model and a significant reformulation and enhancement of CASC2D, was also utilized with NEXRAD products for their case studies of an extreme flood simulation ( Sharif et al. 2010 ) and comparison of the model results with gauge and satellite precipitation data ( Chintalapudi et al. 2012 ). In addition, the NWS formulated and initiated the Distributed Model Intercomparison Project (DMIP) to improve

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

upgrade the nationwide network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar with polarimetric capability. A reasonable expectation is that improvements in rainfall rate estimation will lead to better skill in hydrologic simulation of stream discharge. This is of particular importance in the context of flooding, the second deadliest of all weather-related hazards in the United States; heat is the number one killer ( Ashley and Ashley 2008 ). Accurate forcing data is a prerequisite for

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L. E. Hay, M. P. Clark, R. L. Wilby, W. J. Gutowski Jr., G. H. Leavesley, Z. Pan, R. W. Arritt, and E. S. Takle

estimation at the basin scale. The advent of higher-resolution GCMs may improve the situation; however, hydrologic modeling at the basin scale requires climatological information on scales that are generally far smaller than the typical grid size of even the highest-resolution GCMs commonly used for climate simulations (e.g., Phillips 1995 ). In order to translate (“downscale”) information from the coarse-resolution GCMs to the basin scale for hydrologic modeling, methods are needed that resolve subgrid

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Kirsti Hakala, Nans Addor, and Jan Seibert

therefore usually bias-corrected prior to its use as input to a hydrological model ( Themeßl et al. 2011b ; Teutschbein and Seibert 2012 ; Räisänen and Räty 2013 ). Streamflow is controlled by a wide range of hydrometeorological processes. When streamflow is simulated, the realism of the simulations reflects how well those processes are represented in models. Here we use hydrological modeling to evaluate the atmospheric forcing provided by a recent suite of GCM–RCM combinations. For streamflow to be

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Qiuhong Tang, Taikan Oki, Shinjiro Kanae, and Heping Hu

irrigation in terms of changes to the hydrological cycle. Haddeland et al. (2005) reported on an irrigation scheme in a macroscale hydrological simulation and evaluated the effects of irrigation on the water and energy balances of the Colorado and Mekong River basins. These studies indicated that the subgrid variability caused by human activities has potentially important effects on the surface water and energy balances. However, few complete studies have described the effects of both the subgrid

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Newsha K. Ajami, Qingyun Duan, Xiaogang Gao, and Soroosh Sorooshian

combination techniques are used to obtain consensus simulation?” We will also investigate how the accuracy of the multimodel simulations are influenced by different factors, including 1) the seasonal variations of hydrological processes, 2) number of independent models considered, and 3) accuracy levels of individual member models. The paper is organized as follows: Section 2 overviews different model combination techniques. Section 3 describes the data used in this study. Section 4 presents the

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Satish Bastola and Vasubandhu Misra

that the temporal aggregation of rainfall data has a profound effect on hydrological response, especially on the timing and the magnitude of peak flow, as compared to the spatial resolution of the data. Michaud and Sorooshian (1994) found that aggregating the rainfall data from 4 min to 1 h could lead to a bias (underestimation) of nearly 80% in the simulation of a peak flow event. This study investigates the sensitivity of the hydrological response to some well-known temporal variation of

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Marketa M. Elsner, Subhrendu Gangopadhyay, Tom Pruitt, Levi D. Brekke, Naoki Mizukami, and Martyn P. Clark

choice may cause as much sensitivity in the resulting water balance as the choice of land surface model ( Guo et al. 2006 ), if not more ( Mo et al. 2012 ). Hossain and Anagnostou (2005) and Maggioni et al. (2012) investigated the relative impact of model and rainfall forcing errors in hydrologic simulations by land surface models and found that both together contribute a large amount of the uncertainty in soil moisture estimates. Precipitation appears to cause the greatest sensitivity in runoff

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Mohamed S. Siam, Marie-Estelle Demory, and Elfatih A. B. Eltahir

advantage to emphasize the ability of GCMs to simulate processes that control the hydrological cycle, which provides a good indicator of the general performance of the model. The hydrological cycle depends on several variables, such as wind, specific humidity, precipitation, soil water storage, evaporation, radiation, and clouds. An inaccurate representation of any of these variables would therefore be reflected in the simulation of precipitation, evaporation, and runoff, which highlights the necessity

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