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Mark C. Mastin, Katherine J. Chase, and R. W. Dudley

): the Cathance River, East River, Feather River, South Fork (SF) Flathead River, Naches River, Sprague River, Yampa River, and Sagehen Creek basins. Basin relief as measured from the mean elevations of hydrologic response units (HRUs; land units in the watershed models) ranged from 1887 m in the Feather River basin, California, to 128 m in the Cathance River basin, Maine. The two basins in Colorado (East and Yampa River basins) have the highest mean HRU elevations, which exceed 3500 m ( Table 3

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John Risley, Hamid Moradkhani, Lauren Hay, and Steve Markstrom

assess the effects of potential climate change on mean annual runoff throughout the conterminous United States, Wolock and McCabe ( Wolock and McCabe 1999 ) used a simple water-balance model and output from two atmospheric GCMs. However, their results were uncertain because they were mostly within the range of GCM decade-to-decade variability and GCM model error. To simulate hydrologic climate changes at a watershed scale, downscaled GCM air temperature and precipitation data can be input to

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David M. Bjerklie, Thomas J. Trombley, and Roland J. Viger

, and rely on the simpler Muskingum routing scheme used in the original PRMS. This study is designed to assess the application of PRMS at a regional scale. The model is used to assess the spatial and temporal distribution of air temperature, precipitation, actual evapotranspiration, runoff and streamflow, snowfall, and groundwater recharge across southern and central New England within the Connecticut, Thames, and Housatonic River watersheds. Additionally, the model is used to simulate potential

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Ming-Hsu Li, Ming-Jen Yang, Ruitang Soong, and Hsiao-Ling Huang

groundwater baseflows. Owing to the steep surface slopes in the upstream watersheds over Taiwan, typhoon flood hydrographs normally have large peaks with fast-rising limbs. Although physically based distributed hydrological models are suitable for characterizing these complicated interactions and heterogeneity in mountainous watersheds, improper spatial and temporal resolutions and inadequate interpretation of the land hydrological processes may significantly affect the modeling skill ( Thieken et al

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

and to update the results generated (e.g., Lin et al. 1994 ). Some of these hydrologic models adopt grid-based structures to facilitate distribution of model results over a larger watershed (e.g., Mas et al. 1995 ). More recent works have integrated hydrologic water balance models forced by precipitation and energy flux measured at in situ gauges to produce watershed-scale estimates ( Stillman et al. 2014 ). These analyses are illustrative, but ultimately, the cost of in situ sensors impedes the

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Guoxiang Yang, Laura C. Bowling, Keith A. Cherkauer, Bryan C. Pijanowski, and Dev Niyogi

) macroscale hydrologic model to make it more suitable for urbanized watersheds, resulting in the VIC urban model, as discussed in section 3 . We hypothesize that the streamflow regime is modified by increasing urban intensity, and the VIC urban model can capture these changes in the White River basin as well as the UHI pattern. To test these hypotheses, in section 4 USGS daily streamflow data from 16 small watersheds with different degrees of urbanization in the White River, Indiana (IN), were analyzed

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J. L. Zhang, Y. P. Li, G. H. Huang, C. X. Wang, and G. H. Cheng

parameters cannot be determined through direct observation in the field but can be estimated by calibration against the input–output records of the watershed response, which inevitably contains errors ( Vrugt et al. 2008 ; Joseph and Guillaume 2013 ). Bayesian analysis techniques have been widely employed to incorporate prior information to produce a posterior distribution on which statistical inferences about the model parameters are based ( Robertson and Wang 2012 ; Panday et al. 2014 ; Li et al

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Noriaki Ohara, M. Levent Kavvas, Michael L. Anderson, Z. Q. Chen, and Kei Ishida

.g., Garvert et al. 2005 ; Kure et al. 2013 ). Typically, the variations of the model-estimated precipitation stayed within the variation of the observed values when compared to the corresponding ground observations in the watersheds and monthly distributions by Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data ( Daly et al. 1994 ; Ohara et al. 2011 ; Jang et al. 2012 ; Jang and Kavvas 2015 ). The true physical limit of precipitation, which is the conventional definition of PMP

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Kshitij Parajuli, Scott B. Jones, David G. Tarboton, Lawrence E. Hipps, Lin Zhao, Morteza Sadeghi, Mark L. Rockhold, Alfonso Torres-Rua, and Gerald N. Flerchinger

.jhydrol.2014.02.027 Xia , Y. , T. W. Ford , Y. Wu , S. M. Quiring , and M. B. Ek , 2015 : Automated quality control of in situ soil moisture from the North American soil moisture database using NLDAS-2 products . J. Appl. Meteor. Climatol. , 54 , 1267 – 1282 , https://doi.org/10.1175/JAMC-D-14-0275.1 . 10.1175/JAMC-D-14-0275.1 Xue , B. L. , and Coauthors , 2013 : Modeling the land surface water and energy cycles of a mesoscale watershed in the central Tibetan Plateau during summer

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Andrew J. Newman, Martyn P. Clark, Adam Winstral, Danny Marks, and Mark Seyfried

sections discuss the uni- and multivariate mosaic approaches over RME and then scale the subgrid approaches up to a larger catchment, the Tollgate catchment, which contains several typical 3-km grid spacing (9 km 2 ) mesoscale model grid cells. 4. Catchment disaggregation a. Multivariate disaggregation Most past and current mosaic disaggregation approaches are univariate; they use one land characteristic, commonly vegetation type or elevation, across all grid cells or watersheds. To incorporate a

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