Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
18 Apr 2017
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010
Yu Zhang1, Ming Pan1, Justin Sheffield1, Amanda Siemann1, Colby Fisher1, Miaoling Liang2, Hylke Beck1, Niko Wanders1, Rosalyn MacCracken3, Paul R. Houser3, Tian Zhou4, Dennis P. Lettenmaier5, Yingtao Ma6, Rachel T. Pinker6, Janice Bytheway7, Christian D. Kummerow7, and Eric F. Wood1 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, 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 providing 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. In this study, 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. The resulting long-term (1984–2010), monthly, 0.5° resolution global terrestrial water cycle Climate Data Record (CDR) dataset is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This dataset 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 dataset 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.

Citation: Zhang, Y., Pan, M., Sheffield, J., Siemann, A., Fisher, C., Liang, M., Beck, H., Wanders, N., MacCracken, R., Houser, P. R., Zhou, T., Lettenmaier, D. P., Ma, Y., Pinker, R. T., Bytheway, J., Kummerow, C. D., and Wood, E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2017-192, in review, 2017.
Yu Zhang et al.
Yu Zhang et al.
Yu Zhang et al.


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Short summary
A global data record for all 4 terrestrial water budget variables (precipitation, evapotranspiration, runoff, and total water storage change) at 0.5 degree 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 that the data record is reliable for climate related analysis.
A global data record for all 4 terrestrial water budget variables (precipitation,...