Evaluation of GLDAS-1 and GLDAS-2 Forcing Data and Noah Model Simulations over China at the Monthly Scale

Wen Wang State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

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Wei Cui State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

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Xiaoju Wang Wuxi Hydrology and Water Resources Investigation Bureau of Jiangsu Province, Wuxi, China

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Xi Chen State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

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Abstract

The Global Land Data Assimilation System (GLDAS) is an important data source for global water cycle research. Using ground-based measurements over continental China, the monthly scale forcing data (precipitation and air temperature) during 1979–2010 and model outputs (runoff, water storage, and evapotranspiration) during 2002–10 of GLDAS models [focusing on GLDAS, version 1 (GLDAS-1)/Noah and GLDAS, version 2 (GLDAS-2)/Noah] are evaluated. Results show that GLDAS-1 has serious discontinuity issues in its forcing data, with large precipitation errors in 1996 and large temperature errors during 2000–05. While the bias correction of the GLDAS-2 precipitation data greatly improves temporal continuity and reduces the biases, it makes GLDAS-2 precipitation less correlated with observed precipitation and makes it have larger mean absolute errors than GLDAS-1 precipitation for most months over the year. GLDAS-2 temperature data are superior to GLDAS-1 temperature data temporally and spatially. The results also show that the change rates of terrestrial water storage (TWS) data by GLDAS and the Gravity Recovery and Climate Experiment (GRACE) do not match well in most areas of China, and both GLDAS-1 and GLDAS-2 are not very capable of capturing the seasonal variation in monthly TWS change observed by GRACE. Runoff is underestimated in the exorheic basins over China, and runoff simulations of GLDAS-2 are much more accurate than those of GLDAS-1 for two of the three major river basins of China investigated in this study. Evapotranspiration is overestimated in the exorheic basins in China by both GLDAS-1 and GLDAS-2, whereas the overestimation of evapotranspiration by GLDAS-2 is less than that by GLDAS-1.

Corresponding author address: Wen Wang, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Xikang Road 1, Nanjing 210098, China. E-mail: w.wang@126.com

Abstract

The Global Land Data Assimilation System (GLDAS) is an important data source for global water cycle research. Using ground-based measurements over continental China, the monthly scale forcing data (precipitation and air temperature) during 1979–2010 and model outputs (runoff, water storage, and evapotranspiration) during 2002–10 of GLDAS models [focusing on GLDAS, version 1 (GLDAS-1)/Noah and GLDAS, version 2 (GLDAS-2)/Noah] are evaluated. Results show that GLDAS-1 has serious discontinuity issues in its forcing data, with large precipitation errors in 1996 and large temperature errors during 2000–05. While the bias correction of the GLDAS-2 precipitation data greatly improves temporal continuity and reduces the biases, it makes GLDAS-2 precipitation less correlated with observed precipitation and makes it have larger mean absolute errors than GLDAS-1 precipitation for most months over the year. GLDAS-2 temperature data are superior to GLDAS-1 temperature data temporally and spatially. The results also show that the change rates of terrestrial water storage (TWS) data by GLDAS and the Gravity Recovery and Climate Experiment (GRACE) do not match well in most areas of China, and both GLDAS-1 and GLDAS-2 are not very capable of capturing the seasonal variation in monthly TWS change observed by GRACE. Runoff is underestimated in the exorheic basins over China, and runoff simulations of GLDAS-2 are much more accurate than those of GLDAS-1 for two of the three major river basins of China investigated in this study. Evapotranspiration is overestimated in the exorheic basins in China by both GLDAS-1 and GLDAS-2, whereas the overestimation of evapotranspiration by GLDAS-2 is less than that by GLDAS-1.

Corresponding author address: Wen Wang, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Xikang Road 1, Nanjing 210098, China. E-mail: w.wang@126.com
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  • Chen, Y., Yang K. , Qin J. , Zhao L. , Tang W. , and Han M. , 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res. Atmos., 118, 44664475, doi:10.1002/jgrd.50301.

    • Search Google Scholar
    • Export Citation
  • Gao, Y., Cuo L. , and Zhang Y. , 2014: Changes in moisture flux over the Tibetan Plateau during 1979–2011 and possible mechanisms. J. Climate, 27, 18761893, doi:10.1175/JCLI-D-13-00321.1.

    • Search Google Scholar
    • Export Citation
  • Ghazanfari, S., Pande S. , Hashemy M. , and Sonneveld B. , 2013: Diagnosis of GLDAS LSM based aridity index and dryland identification. J. Environ. Manage., 119, 162172, doi:10.1016/j.jenvman.2013.01.040.

    • Search Google Scholar
    • Export Citation
  • Hao, Z., AghaKouchak A. , Nakhjiri N. , and Farahmand A. , 2014: Global integrated drought monitoring and prediction system. Sci. Data, 1, 140001, doi:10.1038/sdata.2014.1.

    • Search Google Scholar
    • Export Citation
  • Helsel, D. R., and Hirsch R. M. , 2002: Statistical methods in water resources. USGS–TWRI Book 4, Chap. A3, 522 pp. [Available online at https://pubs.usgs.gov/twri/twri4a3/pdf/twri4a3-new.pdf.]

  • Huang, Y., Salama S. , Krol M. , Velde R. , Hoekstra A. , Zhou Y. , and Su Z. , 2013: Analysis of long-term terrestrial water storage variations in the Yangtze River basin. Hydrol. Earth Syst. Sci., 17, 19852000, doi:10.5194/hess-17-1985-2013.

    • Search Google Scholar
    • Export Citation
  • Ji, L., Senay G. B. , and Verdin J. P. , 2015: Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products. J. Hydrometeor., 16, 24632480, doi:10.1175/JHM-D-14-0230.1.

    • Search Google Scholar
    • Export Citation
  • Landerer, F. W., and Swenson S. C. , 2012: Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res., 48, W04531, doi:10.1029/2011WR011453.

    • Search Google Scholar
    • Export Citation
  • National Meteorological Information Center of CMA, 2012: Evaluation of Chinese ground-based precipitation grid dataset (V 2.0) (in Chinese.) Accessed 1 October 2015. [Available online at http://image.data.cma.cn/static/subject/doc/SURF_CLI_CHN_PRE_MON_GRID_0.5_ASSESSMENT.pdf.]

  • Niu, G.-Y., Yang Z.-L. , and Mitchell K. E. , 2011: The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. J. Geophys. Res., 116, D12109, doi:10.1029/2010JD015139.

    • Search Google Scholar
    • Export Citation
  • Proulx, R. A., Knudson M. D. , Kirilenko A. , VanLooy J. A. , and Zhang X. , 2013: Significance of surface water in the terrestrial water budget: A case study in the Prairie Coteau using GRACE, GLDAS, Landsat, and groundwater well data. Water Resour. Res., 49, 57565764, doi:10.1002/wrcr.20455.

    • Search Google Scholar
    • Export Citation
  • Qi, W., Zhang C. , Fu G. , and Zhou H. , 2015: Global Land Data Assimilation System data assessment using a distributed biosphere hydrological model. J. Hydrol., 528, 652667, doi:10.1016/j.jhydrol.2015.07.011.

    • Search Google Scholar
    • Export Citation
  • Ramillien, R., Famiglietti J. S. , and Wahr J. , 2008: Detection of continental hydrology and glaciology signals from GRACE: A review. Surv. Geophys., 29, 361374, doi:10.1007/s10712-008-9048-9.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381394, doi:10.1175/BAMS-85-3-381.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., Chen J. , Kato H. , Famiglietti J. S. , Nigro J. , and Wilson C. R. , 2007: Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeol. J., 15, 159166, doi:10.1007/s10040-006-0103-7.

    • Search Google Scholar
    • Export Citation
  • Rui, H., 2016: Readme document for Global Land Data Assimilation System version 1 (GLDAS-1). Accessed 6 October 2016. [Available online at http://disc.sci.gsfc.nasa.gov/services/grads-gds/gldas.]

  • Seyyedi, H., Anagnostou E. N. , Beighley E. , and McCollum J. , 2015: Hydrologic evaluation of satellite and reanalysis precipitation datasets over a mid-latitude basin. Atmos. Res., 164–165, 3748, doi:10.1016/j.atmosres.2015.03.019.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., Goteti G. , and Wood E. F. , 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 30883111, doi:10.1175/JCLI3790.1.

    • Search Google Scholar
    • Export Citation
  • Syed, T. H., Famiglietti J. S. , Rodell M. , Chen J. , and Wilson C. R. , 2008: Analysis of terrestrial water storage changes from GRACE and GLDAS. Water Resour. Res., 44, W02433, doi:10.1029/2006WR005779.

    • Search Google Scholar
    • Export Citation
  • Wang, A., and Zeng X. , 2012: Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. J. Geophys. Res., 117, D05102, doi:10.1029/2011JD016553.

    • Search Google Scholar
    • Export Citation
  • Wang, F., Wang L. , Koike T. , Zhou H. , Yang K. , Wang A. , and Li W. , 2011: Evaluation and application of a fine-resolution global data set in a semiarid mesoscale river basin with a distributed biosphere hydrological model. J. Geophys. Res., 116, D21108, doi:10.1029/2011JD015990.

    • Search Google Scholar
    • Export Citation
  • Yang, P., and Chen Y. , 2015: An analysis of terrestrial water storage variations from GRACE and GLDAS: The Tianshan Mountains and its adjacent areas, central Asia. Quat. Int., 358, 106112, doi:10.1016/j.quaint.2014.09.077.

    • Search Google Scholar
    • Export Citation
  • Yang, T., Wang C. , Yu Z. , and Xu F. , 2013: Characterization of spatio-temporal patterns for various GRACE- and GLDAS-born estimates for changes of global terrestrial water storage. Global Planet. Change, 109, 3037, doi:10.1016/j.gloplacha.2013.07.005.

    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., Rodell M. , and Olivera F. , 2010: Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme. Water Resour. Res., 46, W06507, doi:10.1029/2009WR007811.

    • Search Google Scholar
    • Export Citation
  • Zawadzki, J., and Kȩdzior M. A. , 2014: Statistical analysis of soil moisture content changes in central Europe using GLDAS database over three past decades. Cent. Eur. J. Geosci., 6, 344353, doi:10.2478/s13533-012-0176-x.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Yao T. , Pu J. , Xiao C. , Kang S. , Hou S. , Ohta T. , and Yabuki H. , 1997: The Features of Hydrological Processes in the Dongkemadi River basin, Tanggula Pass, Tibetan Plateau (in Chinese). J. Glaciol. Geocryology, 19 (3), 214222.

    • Search Google Scholar
    • Export Citation
  • Zhou, X., Zhang Y. , Yang Y. , Yang Y. , and Han S. , 2013: Evaluation of anomalies in GLDAS-1996 dataset. Water Sci. Technol., 67, 17181727, doi:10.2166/wst.2013.043.

    • Search Google Scholar
    • Export Citation
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