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Fan Chen, Wade T. Crow, and Dongryeol Ryu

1. Introduction Surface soil moisture plays a key role in determining the partitioning of surface-incident rainfall between infiltration and surface runoff. As a result, the characterization of prestorm soil moisture states is an important component of most hydrologic prediction systems. With the growing availability of satellite-derived surface soil moisture retrievals ( Naeimi et al. 2009 ; Entekhabi et al. 2010 ; Kerr et al. 2010 ), the application of data assimilation techniques in

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

1. Introduction Because of the importance of accurate streamflow forecasts for water resources planning (e.g., Yao and Georgakakos 2001 ; Hamlet et al. 2002 ), the development of approaches for producing useful streamflow forecasts and the evaluation of these approaches over time has a rich history, going back to at least the 1930s ( Pagano et al. 2004 ). Operational streamflow forecasts generally rely on statistical techniques (e.g., Garen 1992 ). Using various quantities describing the

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

Jeu et al. 2008 ; Draper et al. 2009 , 2011 , 2012 ). At several meteorological centers, including the Canadian Meteorological Centre of Environment Canada (EC), soil moisture is inferred from short-range NWP forecast errors in screen-level temperature and humidity ( Bélair et al. 2003a ; Drusch and Viterbo 2007 ; Mahfouf et al. 2009 ). Soil moisture is used as a sink variable where errors in atmospheric forcing and the land surface model can accumulate over time ( Seuffert et al. 2004

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

because of its persistent memory over longer time scales ( Dirmeyer 2003 ). As a result, numerical weather prediction (NWP) and seasonal climate prediction models require accurate specification of soil moisture conditions for forecast initialization. In addition, estimates of moisture conditions are also required for supporting a variety of societal applications ranging from water resources, agricultural, and natural hazards management to military mobility and famine warning assessments ( Engman 1991

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

regional scales with reduced dependency on costly and uncertain in situ field experiments. Precision agriculture has been largely focused on maximizing field and regional crop yields and associated economic benefits. The tools involved in precision agriculture may also guide regional water resources management, as more accurate modeling and forecasting of water demand for crop production would lead to a more efficient allocation of limited water supplies. Careful monitoring and provision of water

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M. Susan Moran, Bradley Doorn, Vanessa Escobar, and Molly E. Brown

assimilation techniques have been developed to optimally merge information contained in simultaneous active–passive retrievals of surface soil moisture ( Chen et al. 2014 ). Such techniques can potentially form the basis for integrating SMAP data products into next-generation streamflow forecasting systems. Results from these and other studies will elucidate which products will be most appropriate for these activities (given the spatial resolution, frequency/timing, and ease of integration into systems

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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

) land surface scheme ( van den Hurk et al. 2000 ). It considers four layers of soil (0–7, 7–28, 28–100, and 100–289 cm). The assimilation technique used for soil moisture is optimal interpolation (OI; Mahfouf 1991 ). Initially, the OI scheme produces estimates of screen-level temperature and relative humidity by combining synoptic observations (2-m relative humidity and temperature) over land with background estimates (short-range forecasts) from the most recent analysis ( Douville et al. 2000

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Susan Frankenstein, Maria Stevens, and Constance Scott

) to calculate soil moisture and hence soil strength as a function of forecasted weather. The ability of the models to accurately predict soil moisture at a location strongly depends on the quality of the precipitation forecast and the scale of the underlying terrain information. Currently, the scale of the forecast data is 1–15 km. These problems can be partially mollified with assimilation of observations (whether ground based or remote; Margulis et al. 2002 ) and by downscaling techniques

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