Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation

Sujay V. Kumar * Science Applications International Corporation, McLean, Virginia, and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Christa D. Peters-Lidard Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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David Mocko Science Applications International Corporation, McLean, Virginia, and Global Modeling and Assimilation Office, and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Rolf Reichle Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Yuqiong Liu Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Kristi R. Arsenault * Science Applications International Corporation, McLean, Virginia, and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Youlong Xia ** I. M. Systems Group, Inc., and NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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Michael Ek NOAA/NCEP/Environmental Modeling Center, College Park, Maryland

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George Riggs Science Systems and Applications, Inc., Lanham, and Cryospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Ben Livneh Cooperative Institute for Research in Environmental Sciences, Boulder, Colorado

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Michael Cosh Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, McLean, Virginia

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Abstract

The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979–2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the western United States. A quantitative examination of the percentage drought area from root-zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. The results suggest that soil moisture assimilation provides improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.

This article is included in the Advancing Drought Monitoring and Prediction Special Collection.

Corresponding author address: Sujay Kumar, Hydrological Sciences Laboratory, NASA GSFC, Code 617, Greenbelt, MD 20771. E-mail: sujay.v.kumar@nasa.gov

Abstract

The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979–2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the western United States. A quantitative examination of the percentage drought area from root-zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. The results suggest that soil moisture assimilation provides improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.

This article is included in the Advancing Drought Monitoring and Prediction Special Collection.

Corresponding author address: Sujay Kumar, Hydrological Sciences Laboratory, NASA GSFC, Code 617, Greenbelt, MD 20771. E-mail: sujay.v.kumar@nasa.gov
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