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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

“Prairie provinces” (Alberta, Saskatchewan, and Manitoba) and the temperate regions of Ontario and Quebec ( Fig. 1 ). Fig . 1. (top) Agricultural regions of Canada are indicated in gray. Dotted outlines show CARs and solid boundaries indicate provincial borders. (bottom) SMOS satellite pixels used for analysis that have been masked to exclude areas where the majority of the pixel is not agricultural land. Soil moisture estimates from SMOS observations were compiled over the Canadian agricultural

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

. 2002 ; Cox et al. 1986 ). Second, the currently orbiting Soil Moisture Ocean Salinity (SMOS) and planned Soil Moisture Active Passive (SMAP) sensors will provide global measurements of soil moisture at this depth ( Kerr et al. 2001 ; Entekhabi et al. 2010 ). 2. Methods a. Study sites and data selection Nine sites were selected across the southern United States ( Fig. 1 ), composed of seven Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations, one U

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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

model can be integrated at much higher horizontal resolutions, when compared with the full 3D version of the atmospheric model, at a fraction of the computational costs ( Cosgrove et al. 2003 ; Mailhot et al. 2010 ). The principal limitations on the land surface model resolution are mainly a function of the geophysical field forcing datasets. In the current framework, the atmospheric forcing data are taken from the lowest model level in EC’s operational NWP model, which is at a height of roughly 40

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