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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 1
Hydrol. Earth Syst. Sci., 16, 105–119, 2012
https://doi.org/10.5194/hess-16-105-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Latest advances and developments in data assimilation for...

Hydrol. Earth Syst. Sci., 16, 105–119, 2012
https://doi.org/10.5194/hess-16-105-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Jan 2012

Research article | 12 Jan 2012

Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint

B. Li1, D. Toll2, X. Zhan3, and B. Cosgrove4 B. Li et al.
  • 1Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 2Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3NOAA NESDIS Center for Satellite Applications and Research, Camp Springs, MD, USA
  • 4NOAA/NWS Office of Hydrologic Development, Silver Spring, MD, USA

Abstract. Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of near surface soil moisture in a footprint area, and can be used to reduce bias of model estimates (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals, as they are without any scaling or pre-processing, to improve the estimated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

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