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( Dore 2005 ), climatic variability at the local scale is shifting toward intra-annual patterns of extreme weather such as longer growing seasons, larger temperature ranges, increased storm intensity, and longer dry periods ( Frich et al. 2002 ; Zhang et al. 2013 ; Easterling et al. 2000 ). In the early twenty-first century, grassland regions of the United States have experienced a prolonged warm drought ( MacDonald 2010 ) and a shift to larger, more infrequent storms ( Moran et al. 2014 ). This
( Dore 2005 ), climatic variability at the local scale is shifting toward intra-annual patterns of extreme weather such as longer growing seasons, larger temperature ranges, increased storm intensity, and longer dry periods ( Frich et al. 2002 ; Zhang et al. 2013 ; Easterling et al. 2000 ). In the early twenty-first century, grassland regions of the United States have experienced a prolonged warm drought ( MacDonald 2010 ) and a shift to larger, more infrequent storms ( Moran et al. 2014 ). This
-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ) provide global reanalyses for the past three decades (from 1979 onward) at high spatial resolution (spatial resolution of about 80 km for ERA-Interim, ½° and ⅔° spatial resolution in latitude and longitude for MERRA) and with modern data assimilation and modeling systems. Reanalyses of past land–atmosphere conditions constitute a major numerical modeling and data assimilation undertaking. Such atmospheric reanalyses can be
-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ) provide global reanalyses for the past three decades (from 1979 onward) at high spatial resolution (spatial resolution of about 80 km for ERA-Interim, ½° and ⅔° spatial resolution in latitude and longitude for MERRA) and with modern data assimilation and modeling systems. Reanalyses of past land–atmosphere conditions constitute a major numerical modeling and data assimilation undertaking. Such atmospheric reanalyses can be
article. Innovative applications of NASA Earth science data are fostered by the ESD Applied Sciences Program (ASP), which provides support to integrate applications needs into mission planning. The ASP capacity building efforts focus on activities with state and local governments and developing countries to improve capabilities and workforce applying Earth observations. The work of ASP received recent support from the National Research Council’s (NRC) decadal survey ( National Research Council 2007
article. Innovative applications of NASA Earth science data are fostered by the ESD Applied Sciences Program (ASP), which provides support to integrate applications needs into mission planning. The ASP capacity building efforts focus on activities with state and local governments and developing countries to improve capabilities and workforce applying Earth observations. The work of ASP received recent support from the National Research Council’s (NRC) decadal survey ( National Research Council 2007
different models tend to produce similar information on SM temporal variability. In this study, SM values in the WRSI [Eqs. (5) and (6) ] are estimated in three different ways: the original rainfall-driven water balance accounting scheme (bucket), the Noah LSM, and the ECV soil moisture. Given that each different estimate has a unique distribution, and ECV is in units of percent volumetric water content (%VWC), we transformed the soil moisture moments [Eq. (4) ] as recommended by Koster et al
different models tend to produce similar information on SM temporal variability. In this study, SM values in the WRSI [Eqs. (5) and (6) ] are estimated in three different ways: the original rainfall-driven water balance accounting scheme (bucket), the Noah LSM, and the ECV soil moisture. Given that each different estimate has a unique distribution, and ECV is in units of percent volumetric water content (%VWC), we transformed the soil moisture moments [Eq. (4) ] as recommended by Koster et al
of improved observational networks in recent decades, however, has supported the growth of the physical-modeling approach. A now common forecast strategy involves the use of spatially distributed land surface modeling: realistic snow and soil moisture fields are used to initialize the models, which are then integrated into the forecast period with atmospheric forcing, producing streamflow forecasts along the way ( Day 1985 ). The atmospheric forcing can take the form of historical time series at
of improved observational networks in recent decades, however, has supported the growth of the physical-modeling approach. A now common forecast strategy involves the use of spatially distributed land surface modeling: realistic snow and soil moisture fields are used to initialize the models, which are then integrated into the forecast period with atmospheric forcing, producing streamflow forecasts along the way ( Day 1985 ). The atmospheric forcing can take the form of historical time series at
et al. 2013 ). The use of a 4-yr baseline was examined by comparing the average monthly precipitation using meteorological data from only the period when SMOS was operating (2010–13) to a more statistically robust 30-yr baseline from 1981 to 2010, derived according to WMO standard methods ( Fig. 4 ). The 4-yr SMOS period shows some variability both geographically and temporally compared to the 30-yr normal, with April precipitation in Manitoba 12% higher than the 30-yr normal and May
et al. 2013 ). The use of a 4-yr baseline was examined by comparing the average monthly precipitation using meteorological data from only the period when SMOS was operating (2010–13) to a more statistically robust 30-yr baseline from 1981 to 2010, derived according to WMO standard methods ( Fig. 4 ). The 4-yr SMOS period shows some variability both geographically and temporally compared to the 30-yr normal, with April precipitation in Manitoba 12% higher than the 30-yr normal and May
; Norbiato et al. 1996 ; Beck et al. 2000 ; Sheffield et al. 2004 ; Bolten et al. 2010 ; Entekhabi et al. 2010 ). Because of the high spatial and temporal variability of soil moisture, long-term, consistent measurements of soil moisture are not typically available. Passive microwave radiometry has been used to generate estimates of near-surface soil moisture from a number of sensors in the past 30 years ( Jackson 1993 ; Njoku and Entekhabi 1996 ), including the Scanning Multichannel Microwave
; Norbiato et al. 1996 ; Beck et al. 2000 ; Sheffield et al. 2004 ; Bolten et al. 2010 ; Entekhabi et al. 2010 ). Because of the high spatial and temporal variability of soil moisture, long-term, consistent measurements of soil moisture are not typically available. Passive microwave radiometry has been used to generate estimates of near-surface soil moisture from a number of sensors in the past 30 years ( Jackson 1993 ; Njoku and Entekhabi 1996 ), including the Scanning Multichannel Microwave
