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Marco L. Carrera, Stéphane Bélair, and Bernard Bilodeau

model uncertainties on the ensemble streamflow forecasting . Geophys. Res. Lett. , 33 , L12401 , doi: 10.1029/2006GL026855 . Noilhan, J. , and Planton S. , 1989 : A simple parameterization of land surface processes for meteorological models . Mon. Wea. Rev. , 117 , 536 – 549 , doi: 10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2 . Noilhan, J. , and Lacarrère P. , 1995 : GCM grid-scale evaporation from mesoscale modeling . J. Climate , 8 , 206 – 223 , doi: 10

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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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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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