Articles | Volume 19, issue 12
Hydrol. Earth Syst. Sci., 19, 4831–4844, 2015
Hydrol. Earth Syst. Sci., 19, 4831–4844, 2015

Research article 18 Dec 2015

Research article | 18 Dec 2015

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

C. Draper2,1 and R. Reichle1 C. Draper and R. Reichle
  • 1Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, MD, USA
  • 2Universities Space Research Association, Columbia, MD, USA

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

Short summary
A soil moisture assimilation experiment is evaluated separately at sub-seasonal, seasonal, and inter-annual timescales. In addition to previously reported improvements in sub-seasonal scale soil moisture, it is show that such an assimilation can also improve the model soil moisture at seasonal and inter-annual timescales, demonstrating the potential for near-surface soil moisture assimilation to improve model representation of important long-term events, such as droughts.