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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 241-263, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Observations and modeling of land surface water and energy...

Hydrol. Earth Syst. Sci., 22, 241-263, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Jan 2018

Research article | 12 Jan 2018

A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

Yu Zhang1, Ming Pan1, Justin Sheffield1, Amanda L. Siemann1, Colby K. Fisher1, Miaoling Liang2, Hylke E. Beck1, Niko Wanders1, Rosalyn F. MacCracken3, Paul R. Houser3, Tian Zhou4, Dennis P. Lettenmaier5, Rachel T. Pinker6, Janice Bytheway7, Christian D. Kummerow7, and Eric F. Wood1 Yu Zhang et al.
  • 1Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
  • 2National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
  • 3George Mason University, Fairfax, VA 22030, USA
  • 4Pacific Northwest National Laboratory, Richland, WA 99352, USA
  • 5Department of Geography, University of California, Los Angeles, CA 90095, USA
  • 6Department of Meteorology, University of Maryland, College Park, MD 20742, USA
  • 7Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA

Abstract. Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R), and the total water storage change (TWSC) at 0.5° spatial resolution globally and to obtain water budget closure (i.e., to enforce P − ET − R − TWSC = 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984–2010), monthly 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

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Short summary
A global data record for all four terrestrial water budget variables (precipitation, evapotranspiration, runoff, and total water storage change) at 0.5° resolution and monthly scale for the period of 1984–2010 is developed by optimally merging a series of remote sensing products, in situ measurements, land surface model outputs, and atmospheric reanalysis estimates and enforcing the mass balance of water. Initial validations show the data record is reliable for climate related analysis.
A global data record for all four terrestrial water budget variables (precipitation,...