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Randal D. Koster, Gregory K. Walker, Sarith P. P. Mahanama, and Rolf H. Reichle

detailed treatments of a full range of processes (stomatal conductance, interception, baseflow, snow, etc.) serving to determine the fluxes that make up the surface water and energy budgets. The model's unique feature is its explicit treatment of the impacts of subgrid soil moisture variability on the computed evaporation and runoff fluxes. The subgrid variability is keyed to topography and to the model's internal soil moisture prognostic variables, which respond (through mass conservation) both to

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John D. Hottenstein, Guillermo E. Ponce-Campos, Julio Moguel-Yanes, and M. Susan Moran

the U.S. Department of Energy ARM Climate Research Facility and the USDA SCAN network for making these data available. We would also like to thank Dr. Joe Watkins, University of Arizona Department of Mathematics, for his help in the statistical analysis of our results. REFERENCES Baldocchi, D. D. , Xu L. , and Kiang N. , 2004 : How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oak–grass savanna and an annual grassland

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Husayn El Sharif, Jingfeng Wang, and Aris P. Georgakakos

experiment 1 directly influence the evolution of soil moisture in each layer of the soil profile. Water flux downward through the soil profile (via drainage) and upward (via diffusion and root water uptake) is also affected. Lack of predictive skill, due to erroneous water input, would introduce errors with regard to modeling nutrient transport and would cause inaccurate yield estimates. Likewise, errors in solar radiation and temperature data, such as those included in experiment 2, cause errors in crop

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

. Dunne, S. , and Entekhabi D. , 2006 : Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment . Water Resour. Res. , 42 , W01407 , doi: 10.1029/2005WR004334 . Entekhabi, D. , Reichle R. H. , Koster R. D. , and Crow W. T. , 2010a : Performance metrics for soil moisture retrievals and application requirements . J. Hydrometeor. , 11 , 832 – 840 , doi: 10.1175/2010JHM1223.1 . Entekhabi, D. , and Coauthors

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C. Albergel, W. Dorigo, R. H. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, and W. Wagner

America where MERRA-Land indicates a strong positive (wet) trend, while for ERA-Land and SM-MW it is negative (dry). A negative trend observed in independent evaluation datasets (precipitation and NDVI) by Dorigo et al. (2012) appears, however, to confirm the accuracy of the result from ERA-Land. Note also that Reichle et al. (2011) found that MERRA and, to a lesser extent, MERRA-Land show abnormal latent heat fluxes over South America. At the same time, Liu et al. 2013 have analyzed

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Sujay V. Kumar, Kenneth W. Harrison, Christa D. Peters-Lidard, Joseph A. Santanello Jr., and Dalia Kirschbaum

focused primarily on quantifying the improvements to state variables such as root-zone soil moisture that are directly connected to surface soil moisture measurements. Few studies have focused on quantifying the downstream improvements in fluxes, runoff, or streamflow, or on coupled hydrometeorological prediction from soil moisture measurements, and fewer still have attempted to fully quantify economic benefit ( Arnold and Dey 1986 ; Fritz et al. 2008 ). Nevertheless, as the justification of missions

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