Herein, after eliminating well understood atmospheric pressure and tidal mass variations, we provide details of terrestrial water storage flux at scales comparable to medium to large aquifers directly monitored over the continents. These estimates of terrestrial water storage are valuable for predicting biological and agricultural productivity, flooding, and the level of sustainability or depletion of ground water systems. These mass flux estimates fill a significant gap in achieving a more complete understanding of the Earth’s hydrological cycles and long term aquifer sustainability.
To accompany the 10-daily, 4° gridded mass anomaly fields derived from GRACE, numerically modeled soil moisture and snow mass fields from the Global Land Data Assimilation System (GLDAS; Rodell et al., 2004a) are provided with identical spatial and temporal characteristics. The goal of the Global Land Data Assimilation System (GLDAS) is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes (Rodell et al., 2004a). The software, which has been streamlined and parallelized by the Land Information System (LIS) sister project, drives multiple, offline (not coupled to the atmosphere) land surface models, integrates a huge quantity of observation based data, executes globally at high resolutions (2.5° to 1 km), and is capable of producing results in near-real time. A vegetation-based “tiling” approach is used to simulate sub-grid scale variability, with a 1 km global vegetation dataset as its basis. Soil and elevation parameters are based on high resolution global datasets. Observation-based precipitation and downward radiation products, as well as output fields from the best available global coupled atmospheric data assimilation systems, are employed to force the models. Data assimilation techniques for incorporating satellite based hydrological products, including MODIS snow cover and leaf area index, are in various stages of development and implementation.
GLDAS has derived three land surface models (Noah, Mosaic, and the Community Land Model version 2) at 1° spatial resolution using an optimal configuration of parameter, forcing, and assimilation datasets. Soil moisture and snow water equivalent output are then upscaled to match the ten-daily, 4° fields derived from GRACE. Serving these modeled, spatiotemporally analogous fields from the same location as the GRACE based hydrology fields will greatly facilitate the interpretation of the latter. For the values made available herein, we are providing the Noah model.
The availability of both products will greatly facilitate interpretation of the GRACE data from a hydrological perspective. These independent GRACE-derived hydrological mass flux estimates provide a significant advancement to hydrological system monitoring especially when coupled with improved hydrometeorological flux estimates obtained from other remote sensing techniques (MODIS, TRIMM, AMSR). The products from GRACE expands and strengthens the interconnectedness and reuse of key satellite system technologies and make these products available within the wider ESSD science communities
