Improved water balance component estimates through joint assimilation of GRACE water storage and SMOS soil moisture retrievals

Abstract

The accuracy of global water balance estimates is limited by the lack of observations at large scale and the uncertainties of model simulations. Global retrievals of terrestrial water storage (TWS) change and soil moisture (SM) from satellites provide an opportunity to improve model estimates through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scale. For the first time, TWS observations from the Gravity Recovery and Climate Experiment (GRACE) and near‐surface SM observations from the Soil Moisture and Ocean Salinity (SMOS) were jointly assimilated into a water balance model using the Ensemble Kalman Smoother from January 2010 to December 2013 for the Australian continent. The performance of joint assimilation was assessed against open‐loop model simulations and the assimilation of either GRACE TWS anomalies or SMOS SM alone. The SMOS‐only assimilation improved SM estimates but reduced the accuracy of groundwater and TWS estimates. The GRACE‐only assimilation improved groundwater estimates but did not always produce accurate estimates of SM. The joint assimilation typically led to more accurate water storage profile estimates with improved surface SM, root‐zone SM, and groundwater estimates against in situ observations. The assimilation successfully downscaled GRACE‐derived integrated water storage horizontally and vertically into individual water stores at the same spatial scale as the model and SMOS, and partitioned monthly averaged TWS into daily estimates. These results demonstrate that satellite TWS and SM measurements can be jointly assimilated to produce improved water balance component estimates.

On the use of the GRACE normal equation of inter-satellite tracking data for estimation of soil moisture and groundwater in Australia

“…In addition, it is challenging to improve surface soil moisture varying rapidly in time, using a monthly mean GRACE observation. Tian et al (2017) utilized the satellite soil moisture observation from Soil Moisture and Ocean Salinity (SMOS, Kerr et al, 2001) in addition to GRACE data for their data assimilation and showed a clear improvement in the top soil moisture estimate. The GC approach with complementary observations at higher temporal resolution should be considered, particularly to enhance the surface soil moisture computation.…”

“…Australia experiences significant climate variability; for example, the Millennium Drought starting from late 1990 (Van Dijk et al, 2013) and extremely wet conditions during several La Niña episodes (Trenberth, 2012;Han, 2017). These periods are referred as "Big Dry" and "Big Wet" (Ummenhofer et al, 2011;Xie et al, 2016).…”

“…In data assimilation, the GRACE uncertainty is often derived empirically, not necessarily reflecting the actual GRACE error characteristics (e.g. Zaitchik et al, 2008;Tangdamrongsub et al, 2015;Tian et al, 2017). For example, Girotto et al (2016) used the L3 product and showed that it was necessary to adjust GRACE observation and its uncertainty in order to make their water storage estimates more accurate.…”

“…For example, Girotto et al (2016) used the L3 product and showed that it was necessary to adjust GRACE observation and its uncertainty in order to make their water storage estimates more accurate. Similarly, Tian et al (2017) reported the need for the application of a scale factor to GRACE uncertainty (from mascon, mass concentration, product) in their GRACE assimilation process. It is apparent that the use of post-processed GRACE products often requires data tuning, leading possibly to an integration of the altered gravity information into the data assimilation system.…”

Water Loss Due to Increasing Planted Vegetation over the Badain Jaran Desert, China

“…As an integrative measure of vertical changes in total water storage, the results of GRACE trend analyses have been validated by multi-source data such as finer satellite observations, modelling data, and lots of in-situ data [74][75][76][77][78][79][80][81][82][83][84][85][86][87]. The BJD region is characterized as having a typical arid or hyper arid climate, with the annual potential evaporation exceeding over four times that of the amount of annual precipitation [88].…”