author = "Li, Bailing and Rodell, Matthew and Kumar, Sujay and Beaudoing, 
                         Hiroko and Getirana, Augusto and Zaitchik, Benjamin F. and 
                         Gon{\c{c}}alves, Lu{\'{\i}}s Gustavo Gon{\c{c}}alves de and 
                         Cossetin, Camila and Bhanja, Soumendra Nath and Mukherjee, Abhijit 
                         and Tian, Siyuan and Tangdamrongsub, Nattharchet and Long, Di and 
                         Nanteza, Jamiat and Lee, Jejung and Policelli, Frederick S. and 
                         Ibrahim, Goni and Djoret, Daira and Bila, Mohammed D. and De 
                         Lannoy, Gabrielle and Mocko, David M. and Steele-Dunne, Susan C. 
                         and Save, Himanshu and Bettadpur, Srinivas V.",
          affiliation = "{University of Maryland College Park} and {NASA Goddard Space 
                         Flight Center} and SAIC and SAIC and {NASA Goddard Space Flight 
                         Center} and {Johns Hopkins University} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and Climatempo and {Indian Institute 
                         of Technology Kharagpur} and {Indian Institute of Technology 
                         Kharagpur} and {Australian National University} and School of 
                         Engineering, University of Newcastle and {Tsinghua University} and 
                         {} and {Univ Missouri-Kansas City} and {NASA Goddard Space Flight 
                         Center} and {} and {Lake Chad Basin Commission} and {} and 
                         {Katholieke Universiteit Leuven} and SAIC and {Technische 
                         Universiteit Delft} and {University of Texas at Austin} and 
                         {University of Texas at Austin}",
                title = "Global groundwater storage estimates through assimilation of GRACE 
                         data into a land surface model",
                 year = "2018",
         organization = "AGU Fall Meeting",
             abstract = "Groundwater is one of the most important natural resources for the 
                         global community, with more than 2 billion people relying 
                         exclusively on groundwater for drinking water and 43% of 
                         irrigation water being supplied by aquifers. However, the scarcity 
                         of groundwater variation data at the global scale hinders our 
                         ability to monitor and manage groundwater resources effectively. 
                         The terrestrial water storage (TWS) changes derived from the 
                         Gravity Recovery and Climate Experiment (GRACE) satellite mission 
                         have shown great promise in detecting groundwater storage changes 
                         around the world. The application of GRACE data for groundwater 
                         hydrology can be facilitated by GRACE data assimilation, which 
                         constrains model estimates while providing vertical disaggregation 
                         and spatial downscaling. Building upon previous studies at 
                         regional to continental scales, this study assimilates a 
                         state-of-the-art GRACE TWS product into NASAs Catchment land 
                         surface model (CLSM) at the global scale with an improved ensemble 
                         smoother. The GRACE data were derived using a regional mass 
                         concentration approach with time variable constraints applied 
                         during the inversion of satellite ranging observations (as opposed 
                         to after inversion) to better preserve the information in those 
                         measurements. Time series of in situ data from nearly 4,000 wells 
                         located in different continents and climate zones were obtained to 
                         evaluate the impact of GRACE data assimilation on CLSM estimated 
                         groundwater. The comparison shows that GRACE data assimilation has 
                         a strong positive impact on simulated groundwater storage, with 
                         estimation errors reduced by 36% and 10% and correlation improved 
                         by 16% and 22% at the regional and point scales, respectively. The 
                         improvements are climate dependent, with the largest observed in 
                         regions with substantial interannual variability in precipitation, 
                         where simulated groundwater responds too strongly to changes in 
                         atmospheric forcing. We discuss the impacts of GRACE data 
                         assimilation on the temporal and spatial variability of TWS and 
                         groundwater storage and model deficiencies, including the lack of 
                         groundwater pumping, that limit its ability to distribute 
                         assimilated TWS properly. Application of this dataset for 
                         groundwater drought monitoring is also described.",
  conference-location = "Washington, D. C.",
      conference-year = "10-14 dec.",
             language = "en",
        urlaccessdate = "15 jan. 2021"