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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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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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Fan Chen, Wade T. Crow, and Dongryeol Ryu

al. 2012 ) and are often the only source of precipitation data available for ungauged basins. However, real-time products with relatively low latency (less than daily) typically contain significant bias and errors that can propagate into other model variables during operational streamflow forecasting. Left uncorrected, such errors can significantly impact subsequent hydrologic model predictions ( Harris et al. 2007 ; Li et al. 2009 ; Pan et al. 2010 ; Gebregiorgis et al. 2012 ). Recent work

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Amy McNally, Gregory J. Husak, Molly Brown, Mark Carroll, Chris Funk, Soni Yatheendradas, Kristi Arsenault, Christa Peters-Lidard, and James P. Verdin

1. Introduction Soil moisture is a critical variable for weather and climate forecasting and early warning for natural disasters like drought, floods, landslides, and fire. Soil moisture also plays an important role in the early warning of human health concerns like hunger and malaria. The Soil Moisture Active Passive (SMAP) mission ( https://smap.jpl.nasa.gov/ ) aims to provide high-quality soil moisture data and enhance predictive models for many applications. However, new tools need to be

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

precipitation forcing and assimilation of L-band brightness temperatures in the Canadian Land Data Assimilation System (Carrera et al. 2014, manuscript submitted to J. Hydrometeor. ). The Institute for Climate and Society (IRI) at Columbia University tested a data assimilation framework for forecasting yields of grain crops in Africa and reported the potential to detect irrigation applications otherwise not possible through model simulations ( Ines et al. 2013 ; Das et al. 2014, manuscript submitted to J

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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

1. Introduction The importance of soil moisture in the global climate system has recently been underlined by the Global Climate Observing System (GCOS) Programme endorsing soil moisture as an Essential Climate Variable (ECV). It is a crucial variable for numerical weather prediction (NWP) and climate projections because it plays a key role in hydrological processes. A good representation of soil moisture conditions can therefore help improve the forecasting of precipitation, droughts, and

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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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Catherine Champagne, Andrew Davidson, Patrick Cherneski, Jessika L’Heureux, and Trevor Hadwen

vegetation growth season (April–September) and monthly during the late winter/early spring (February/March) and fall (October/November). Climate analysts review numerous datasets, including meteorological data, seasonal weather forecasts, yield predictions, media reports, and producer-reported climate impacts collected through the Agroclimate Impact Reporter (AIR; www.agr.gc.ca/air ). Reports consist of a textual description describing risk conditions in each region and the evidence base for these

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