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

1. Introduction Agricultural risk assessment is a key tool for determining potential and actual losses in food production that result from climatic extremes such as deficits and excesses of moisture in the soil and at the surface. Soil moisture is a key determinant of crop production, impacting field accessibility for seeding, harvest, and field management; sustaining productive crop growth; and often determining vulnerability of crops to disease and pests. Characterizing soil moisture and soil

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

forecasting; Roulin (2007) for flood forecasting; and McCollor and Stull (2008) for reservoir management. Such “decision theoretic” approaches have been explored for water resources management applications ( James and Freeze 1993 ; Hobbs 1997 ; Harrison 2007 ; Wang and Harrison 2013 ). The article is organized as follows. A brief description of the modeling system, the drought and flood risk assessment methodology, and the decision-theory-based economic model is presented in section 2 . This is

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

drought and risk assessments by factoring in both science and societal impacts ( Kumar et al. 2014a ). Simulated data similar to SMAP products were used in agricultural models to show the usefulness of soil moisture for crop yield estimation at sites where the full time sequence of precipitation and other critical weather variables were not available or subject to measurements errors (El Sharif et al. 2014, manuscript submitted to J. Hydrometeor. ) and the increase in streamflow forecast skill

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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

the time series for that region ( Fig. 4 ) shows that time series agree well when there were the “irregular and insufficient rains” in 2004 ( ) and 2009 ( ). We found similar results, in other regions in other years, where products agreed on severe agricultural droughts and diverged under more moderate conditions. Fig . 3. Map of WRSI anomalies from different SM products in 2002. In Niger there are

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

resources for agricultural use is critical as agriculture demands a large fraction of total water use in the United States and the world. In 2005, irrigation in the United States consumed 128 billion gallons per day, accounting for 37% of all freshwater withdrawals and 62% of all freshwater withdrawals excluding thermoelectric withdrawals ( Kenny et al. 2009 ). The 2013 National Climate Assessment (NCA) indicates that under the A2 emissions scenario, U.S. freshwater withdrawals will increase by 25

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Fan Chen, Wade T. Crow, and Dongryeol Ryu

filtering that assimilates both and into the API model for rainfall correction. 4. Results Prior to consideration of SAC streamflow results, the improvement of TMPA 3B40RT rainfall via SMART application is evaluated in section 4a . This is followed by an assessment of the bias correction scheme described above on SAC streamflow results in section 4b . Finally, streamflow data assimilation results for the five separate cases described in section 3c are presented in section 4c . a. SMART rainfall

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

extrapolation is subject to risk.) With this important caveat, Fig. 9 provides, for each basin and season addressed in Fig. 4 , estimates of the lower bound of r W-truth as produced with the procedure. The calculations imply, for example, that for the Ohio River basin, the model-based soil moistures used in our analyses, which are based on historical time series of meteorological forcing, compare with the true (unknown) soil moistures there with a correlation coefficient higher than 0.35 on 1 January

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