ALBERT

Description: The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual time scales Hydrology and Earth System Sciences Discussions, 12, 7971-8004, 2015 Author(s): C. Draper and R. Reichle Nine years 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 (SM short ), mean seasonal (SM seas ) and inter-annual (SM long ) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10 −3 m 3 m −3 ) 2 , of which 19 % was in SM long , 26 % in SM seas , and 55 % in SM short . 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 SM long , 24 % in SM seas , and 38 % in SM short . 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 SM short , 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 SM seas 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 one 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 one year of data is available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, but locally and in extreme years there is a risk of increased errors.