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

-real-time measurements of soil moisture conditions at the surface. The Soil Moisture Ocean Salinity (SMOS) mission was launched in November 2009 with goals that included the direct measurement of soil moisture at the earth’s surface and the integration of these measurements into land surface models to estimate root zone soil moisture conditions ( Kerr et al. 2001 ). Beginning with the 2010 growing season and continuing to the end of the 2013 growing season, Agriculture and Agri-Food Canada (AAFC) piloted the use of

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Susan Frankenstein, Maria Stevens, and Constance Scott

resolution by running a land surface model [in this case, the community Noah land surface model (LSM; Chen et al. 1996 ; Ek et al. 2003 ) within Land Information System (LIS; Kumar et al. 2006 , 2008 ; Peters-Lidard et al. 2007 )] for a period of time (usually 5 years). From this time period, soil moisture maps are constructed to represent four climatological conditions: the “dry” (driest 30-day period in an average rainfall year), “average” (average 180-day period in an average rainfall year), “wet

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

, ecosystem responses to precipitation changes are not necessarily a simple combination of the responses of the individual factors ( Shaw et al. 2002 ; Zhang et al. 2013 ; Knapp et al. 2008 ). Conditions that affect soil moisture and increase production through 2 years can cause simplifications in the food web after 5 years ( Suttle et al. 2007 ). Chronic intense storms will alter both the mean and temporal variability of soil moisture, resulting in long-term shifts in community composition ( Knapp et

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

current state of a system (e.g., snow amount, soil moisture, and climate indices), calibrated regressions are applied that transform these quantities into streamflow forecasts. The historical use of these statistical techniques is arguably a reflection of historical limitations in our ability to model accurately the physical processes that generate streamflow—in particular our ability to provide the high-resolution forcing and boundary condition data needed to support the physical modeling. The advent

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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

; Drusch and Viterbo 2007 ). To better represent the land surface in environmental prediction and assimilation systems, the Canadian Land Data Assimilation System (CaLDAS) is being developed at EC’s Meteorological Research Division (MRD). It is planned that CaLDAS will provide the initial conditions, including soil moisture, surface temperature, snow cover, and vegetation properties to both EC’s deterministic and ensemble prediction systems. Much of the early operational development of CaLDAS focused

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