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

Abstract

Abstract. An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation ( TWS) derived from the Gravity Recovery and Climate Experiment (GRACE) into land surface models (LSMs). However, the GRACE-derived TWS was generally computed from the high-level products (e.g. time-variable gravity fields, i.e. level 2, and land grid from the level 3 product). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by a posteriori filters and a lack of error covariance information. The postprocessing of GRACE data might lead to the undesired alteration of the signal and its statistical property. This study uses the GRACE least-squares normal equation data to exploit the GRACE information rigorously and negate these limitations. Our approach combines GRACE's least-squares normal equation (obtained from ITSG-Grace2016 product) with the results from the Community Atmosphere Biosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the GRACE raw data. The GRACE-combined (GC) approach is developed for optimal least-squares combination and the approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins. The results are validated against the satellite soil moisture observation and the in situ groundwater data. Comparing to CABLE, we demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement on seasonal and inter-annual timescales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates.

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

“…All models were run for the period 1971-2010 with input climate data provided by the Global Soil Wetness Project Phase 3, (GSWP3; [58]). GSWP3 has been used as a forcing dataset in several other recent GHM and LSM studies [2,26,57,59,60] (see SI, section 1.3, for an explanation of why we used this forcing dataset specifically).…”

A hydrological model for root zone water storage simulation on a global scale

“…GSWP3 data set is generated based on the Twentieth Century Reanalysis project (Compo et al, 2011). It has been proved to be able to represent realistic sub-monthly variability 10 over the entire 20th century and has been used as a forcing data set in several other hydrological modelling studies (Veldkamp et al, 2017;Masaki et al, 2017;Liu et al, 2017;Tangdamrongsub et al, 2018). The climate variables used in this study includes precipitation, minimum temperature, maximum temperature, relative humidity, surface downwelling longwave radiation, surface downwelling shortwave radiation and wind speed at 10 meter.…”

Detection of groundwater storage variability based on GRACE and CABLE model in the Murray-Darling Basin

“…085 and 2. 872 m, from top to bottom, respectively [11]. More details about the CABLE model refer to [16][17].…”

“…Therefore, the output data from CABLE model can provide accurate information of surface water and flux field such as SWE and SM like other hydrological models. Several studies have applied this model to estimate the GWS changes combining with GRACE data [11].…”