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Journal of Meteorological Research

, Volume 33, Issue 2, pp 159–189 | Cite as

Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects

  • Youlong Xia
  • Zengchao HaoEmail author
  • Chunxiang Shi
  • Yaohui Li
  • Jesse Meng
  • Tongren Xu
  • Xinying Wu
  • Baoqing Zhang
Special Collection on Development and Applications of Regional and Global Land Data Assimilation Systems
  • 6 Downloads

Abstract

Since the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS) were established in 2004, significant progress has been made in development of regional and global LDASs. National, regional, project-based, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications (from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure, parameters, and forcing are discussed.

Key words

land data assimilation system (LDAS) regional and global LDASs in-situ observation satellite retrieval land surface model (LSM) 

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Notes

Acknowledgment

The authors thank Eric Luebehusen of U.S. Department of Agriculture who helped us generate Fig. 5. We acknowledge Mary Hart for proofreading and editing our first draft, Holly Norton and Roshan Shrestha for the EMC internal review, and three anonymous reviewers for valuable comments.

References

  1. AghaKouchak, A., A. Farahmand, F. S. Melton, et al., 2015: Remote sensing of drought: Progress, challenges and opportunities. Rev. Geophy., 53, 452–480, doi: 10.1002/2014RG000 456.CrossRefGoogle Scholar
  2. Albergel, C., J.-C. Calvet, J.-F. Mahfouf, et al., 2010: Monitoring of water and carbon fluxes using a land data assimilation system: A case study for southwestern France. Hydrol. Earth Syst. Sci., 14, 1109–1124, doi: 10.5194/hess-14-1109-2010.CrossRefGoogle Scholar
  3. Albergel, C., E. Dutra, S. Munier, et al., 2018: ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? Hydrol. Earth Syst. Sci., 22, 3515–3532, doi: 10.5194/hess-22-3515-2018.CrossRefGoogle Scholar
  4. Al-Yaari, A., J.-P. Wigneron, A. Ducharne, et al., 2014: Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land). Remote Sen. Environ., 152, 614–626, doi: 10.1016/j.rse.2014.07.013.CrossRefGoogle Scholar
  5. Bai, W. K., X. L. Gu, S. L. Li, et al., 2018: The performance of multiple model-simulated soil moisture datasets relative to ECV satellite data in China. Water, 10, 1384, doi: 10.3390/w10101384.CrossRefGoogle Scholar
  6. Baldocchi, D., E. Falge, L. H. Gu, et al., 2001: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Amer. Meteor. Soc., 82, 2415–2434, doi: 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.CrossRefGoogle Scholar
  7. Balsamo, G., J.-F. Mahfouf, S. Bélair, et al., 2007: A land data assimilation system for soil moisture and temperature: An information content study. J. Hydrometeor., 8, 1225–1242, doi: 10.1175/2007JHM819.1.CrossRefGoogle Scholar
  8. Balsamo, G., C. Albergel, A. Beljaars, et al., 2015: ERA-interim/land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci., 19, 389–407, doi: 10.5194/hess-19-389-2015.CrossRefGoogle Scholar
  9. Bateni, S. M., and D. Entekhabi, 2012: Relative efficiency of land surface energy balance components. Water Resour. Res., 48, W04510, doi: 10.1029/2011WR011357.Google Scholar
  10. Beck, H. E., A. de Roo, and A. I. J. M. van Dijk, 2015: Global maps of streamflow characteristics based on observations from several thousand catchments. J. Hydrometeor., 16, 1478–1501, doi: 10.1175/JHM-D-14-0155.1.CrossRefGoogle Scholar
  11. Beck, H. E., N. Vergopolan, M. Pan, et al., 2017a: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci., 21, 6201–6217, doi: 10.5194/hess-21-6201-2017.CrossRefGoogle Scholar
  12. Beck, H. E., A. I. J. M. van Dijk, V. Levizzani, et al., 2017b: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21, 589–615, doi: 10.5194/hess-21-589-2017.CrossRefGoogle Scholar
  13. Beck, H. E., E. F. Wood, M. Pan, et al., 2018: MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. Bull. Amer. Meteor. Soc. doi: 10.1175/BAMS-D-17-0138.1.Google Scholar
  14. Bélair, S., L.-P. Crevier, J. Mailhot, et al., 2003a: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: Warm season results. J. Hydrometeor., 4, 352–370, doi: 10.1175/1525-7541(2003)4<352:OIOTIL>2.0.CO;2.CrossRefGoogle Scholar
  15. Bélair, S., R. Brown, J. Mailhot, et al., 2003b: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part II: Cold season results. J. Hydrometeor., 4, 371–386, doi: 10.1175/1525-7541(2003)4<371:OIOTIL>2.0.CO;2.CrossRefGoogle Scholar
  16. Bell, J. E., M. A. Palecki, C. B. Baker, et al., 2013: U.S. climate reference network soil moisture and temperature observations. J. Hydrometeor., 14, 977–988, doi: 10.1175/JHM-D-12-0146.1.CrossRefGoogle Scholar
  17. Berg, A. A., J. S. Famiglietti, J. P. Walker, et al., 2003: Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes. J. Geophys. Res. Atmos., 108, 4490, doi: 10.1029/2002JD003334.CrossRefGoogle Scholar
  18. Best, M. J., M. Pryor, D. B. Clark, et al., 2011: The Joint UK Land Environment Simulator (JULES), model description–Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677–699, doi: 10.5194/gmd-4-677-2011.CrossRefGoogle Scholar
  19. Best, M. J., G. Abramowitz, H. R. Johnson, et al., 2015: The plumbing of land surface models: Benchmarking model performance. J. Hydrometeor., 16, 1425–1442, doi: 10.1175/ JHM-D-14-0158.1.CrossRefGoogle Scholar
  20. Bowling, L. C., D. P. Lettenmaier, B. Nijssen, et al., 2003: Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e): 1: Experiment description and summary intercomparisons. Glob. Planet. Change, 38, 1–30, doi: 10.1016/S0921-8181(03)00003-1.CrossRefGoogle Scholar
  21. Brocca, L., S. Hasenauer, T. Lacava, et al., 2011: Soil moisture estimation through ASCAT and AMSR-E sensors: An inter-comparison and validation study across Europe. Remote Sens. Environ., 115, 3390–3408, doi: 10.1016/j.rse.2011.08.003.CrossRefGoogle Scholar
  22. Bromwich, D. H., and S. H. Wang, 2005: Evaluation of the NCEP-NCAR and ECMWF 15- and 40-yr reanalyses using rawin-sonde data from two independent Arctic field experiments. Mon. Wea. Rev., 133, 3562–3578, doi: 10.1175/MWR3043.1.CrossRefGoogle Scholar
  23. Broxton, P. D., X. B. Zeng, D. Sulla-Menashe, et al., 2014: A global land cover climatology using MODIS data. J. Appl. Meteor. Climatol., 53, 1593–1605, doi: 10.1175/JAMC-D-13-0270.1.CrossRefGoogle Scholar
  24. Burnash, R. J. C., R. L. Ferral, and R. A. McGuire, 1973: A Generalized Streamflow Simulation System-Conceptual Modeling for Digital Computer. Technical Report, Joint Fed.–State River Forecast Cent., U. S. Natl. Weather Serv. and California Dep. of Water Resoure, Sacramento, CA, USA, 204 pp.Google Scholar
  25. Carrera, M. L., S. Bélair, and B. Bilodeau, 2015: The Canadian land data assimilation system (CaLDAS): Description and synthetic evaluation study. J. Hydrometeor., 16, 1293–1314, doi: 10.1175/JHM-D-14-0089.1.CrossRefGoogle Scholar
  26. Case, J. L, S. V. Kumar, J. Srikishen, et al., 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high-resolution ini-tializationof the surface state. Wea. Forecasting, 26, 785–807, doi: 10.1175/2011WAF2222455.1.CrossRefGoogle Scholar
  27. Case, J. L., F. J. Lafontaine, J. R. Bell, et al., 2014: A real-time MODIS vegetation product for land surface and numerical weather prediction models. IEEE Trans. Geosci. Remote Sens., 52, 1772–1786, doi: 10.1109/TGRS.2013.2255059.CrossRefGoogle Scholar
  28. Chakrabarti, S., T. Bongiovanni, T. Judge, et al., 2017: Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 3867–3879, doi: 10.1109/JSTARS.2014.2315999.CrossRefGoogle Scholar
  29. Chaudhuri, A. H., R. M. Ponte, and A. T. Nguyen, 2014: A comparison of atmospheric reanalysis products for the Arctic Ocean and implications for uncertainties in air–sea fluxes. J. Climate, 27, 5411–5421, doi: 10.1175/JCLI-D-13-00424.1.CrossRefGoogle Scholar
  30. Chen, F., Z. Janjic, and K. Mitchell, 1997: Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Bound.-Layer Meteor., 85, 391–421, doi: 10.1023/A:1000531001463.CrossRefGoogle Scholar
  31. Chen, F., K. W. Manning, M. A. LeMone, et al., 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46, 694–713, doi: 10.1175/JAM2463.1.CrossRefGoogle Scholar
  32. Chen, Y. Y., K. Yang, J. Qin, et al., 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, 4466–4475, doi: 10.1002/jgrd.50301.CrossRefGoogle Scholar
  33. Clark, D. B., L. M. Mercado, S. Sitch, et al., 2011: The Joint UK Land Environment Simulator (JULES), model description. Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev., 4, 701–722, doi: 10.5194/gmd-4-701-2011.CrossRefGoogle Scholar
  34. Clark, M. P., B. Nijssen, J. D. Lundquist, et al., 2015a: A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resour. Res., 51, 2498–2514, doi: 10.1002/2015WR017198.CrossRefGoogle Scholar
  35. Clark, M. P., B. Nijssen, J. D. Lundquist, et al., 2015b: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies. Water Resour. Res., 51, 2515–2542, doi: 10.1002/2015WR017200.CrossRefGoogle Scholar
  36. Clewley, D., J. B. Whitcomb, R. Akbar, et al., 2017: A method for upscaling in situ soil moisture measurements to satellite footprint scale using random forests. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 2663–2673, doi: 10.1109/JS TARS.2017.2690220.CrossRefGoogle Scholar
  37. Cloke, H. L., and F. Pappenberger, 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613–626, doi: 10.1016/j.jhydrol.2009.06.005.CrossRefGoogle Scholar
  38. Compo, G. P., J. S. Whitaker, P. D. Sardeshmukh, et al., 2011: The Twentieth Century Reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 1–28, doi: 10.1002/qj.776.CrossRefGoogle Scholar
  39. Cosgrove, B. A., D. Lohmann, K. E. Mitchell, et al., 2003a: Realtime and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res. Atmos., 108, 8842, doi: 10.1029/2002JD003118.CrossRefGoogle Scholar
  40. Cosgrove, B. A., D. Lohmann, K. E. Mitchell, et al., 2003b: Land surface model spin-up behavior in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res. Atmos., 108, 8845, doi: 10.1029/2002JD003316.CrossRefGoogle Scholar
  41. Crow, W. T., and E. F. Wood, 2003: The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97. Adv. Water Resour., 26, 137–149, doi: 10.1016/S0309-1708(02)00088-X.CrossRefGoogle Scholar
  42. Crow, W. T., A. A. Berg, M. H. Cosh, et al., 2012: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys., 50, RG2002, doi: 10.1029/2011RG000372.CrossRefGoogle Scholar
  43. Cui, C. Y., J. Xu, and J. Y. Zeng, 2018: Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sens., 10, 33, doi: 10.3390/ rs10010033.CrossRefGoogle Scholar
  44. Dai, A. G., 2008: Temperature and pressure dependence of the rain–snow phase transition over land and ocean. Geophys. Res. Lett., 35, L12802, doi: 10.1029/2008GL033295.CrossRefGoogle Scholar
  45. Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1023, doi: 10.1175/BAMS-84-8-1013.CrossRefGoogle Scholar
  46. de Goncalves, L. G. G., W. J. Shuttleworth, E. J. Burke, et al., 2006: Toward a South America land data assimilation system: Aspects of land surface model spin-up using the simplified simple biosphere. J. Geophys. Res. Atmos., 111, D17110, doi: 10.1029/2005JD006297.CrossRefGoogle Scholar
  47. de Rosnay, P., 2017: Land Surface Data for Land Surface Analysis. ECMWF Data Assimilation Training Course, ECMWF, Reading, UK, 45 pp. Available at https://software.ecmwf.int/wiki/display/LDAS/LDAS+Home?preview=/27398058/76382811/Land_satellite_NWP_SAF_TC_2017.pdf.Google Scholar
  48. de Rosnay, P., G. Balsamo, C. Albergel, et al., 2014: Initialization of land surface variables for numerical weather prediction. Surv. Geophys., 35, 607–621, doi: 10.1007/s10712-012-9207-x.CrossRefGoogle Scholar
  49. de Wit, A. J. W., and C. A. van Diepen, 2007: Crop model data assimilation with the ensemble Kalman filter for improving regional crop yield forecasts. Agric. Forest Meteor., 146, 38–56, doi: 10.1016/j.agrformet.2007.05.004.CrossRefGoogle Scholar
  50. Decker, M., M. A. Brunke, Z. Wang, et al., 2012: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Climate, 25, 1916–1944, doi: 10.1175/JCLI-D-11-00004.1.CrossRefGoogle Scholar
  51. Dee, D. P., M. Balmaseda, G. Balsamo, et al., 2014: Toward a consistent reanalysis of the climate system. Bull. Amer. Meteor. Soc., 95, 1235–1248, doi: 10.1175/BAMS-D-13-00043.1.CrossRefGoogle Scholar
  52. Dente, L., G. Satalino, F. Mattia, et al., 2008: Assimilation of leaf area index derived from ASAR and MERIS data into CERES-wheat model to map wheat yield. Remote Sens. Environ., 112, 1395–1407, doi: 10.1016/j.rse.2007.05.023.CrossRefGoogle Scholar
  53. Derin, Y., and K. K. Yilmaz, 2014: Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeor., 1 5, 1498–1516, doi: 10.1175/JHM-D-13-0191.1.CrossRefGoogle Scholar
  54. Dharssi, I., K. J. Bovis, B. Macpherson, et al., 2011: Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrol. Earth Syst. Sci., 15, 2729–2746, doi: 10.5194/hess-15-2729-2011.CrossRefGoogle Scholar
  55. Dickinson, R. E., A. Henderson-Sellers, and P. J. Kennedy, 1993: Biosphere–Atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model. NCAR Technical Note NCAR/TN-387+STR, NCAR, Boulder, 72 pp, doi: 10.5065/D67W6959.Google Scholar
  56. Dietz, A. J., C. Kuenzer, U. Gessner, et al., 2012: Remote sensing of snow–a review of available methods. Int. J. Remote Sens., 33, 4094–4134, doi: 10.1080/01431161.2011.640964.CrossRefGoogle Scholar
  57. Ding, B. H., K. Yang, J. Qin, et al., 2014: The dependence of precipitation types on surface elevation and meteorological conditions and its parameterization. J. Hydrol., 513, 154–163, doi: 10.1016/j.jhydrol.2014.03.038.CrossRefGoogle Scholar
  58. Dirmeyer, P. A., A. J. Dolman, and N. Sato, 1999: The global soil wetness project. Bull. Amer. Meteor. Soc., 80, 851–878, doi: 10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO;2.CrossRefGoogle Scholar
  59. Dirmeyer, P. A., X. Gao, M. Zhao, et al., 2006: GSWP-2: Mul-timodel analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87, 1381–1398, doi: 10.1175/BAMS-87-10-1381.CrossRefGoogle Scholar
  60. Dorigo, W. A., W. Wagner, R. Hohensinn, et al., 2011: The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 1675–1698, doi: 10.5194/hess-15-1675-2011.CrossRefGoogle Scholar
  61. Dorigo, W. A., A. Xaver, M. Vreugdenhil, et al., 2013: Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone Journal, 12, 1–21, doi: 10.2136/vzj2012.0097.CrossRefGoogle Scholar
  62. Doycheva, K., G. Horn, C. Koch, et al., 2017: Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning. Adv. Eng. Inform., 33, 427–439, doi: 10.1016/j.aei.2016.11.001.CrossRefGoogle Scholar
  63. Draper, C. S., R. H. Reichle, and R. D. Koster, 2018: Assessment of MERRA-2 land surface energy flux estimates. J. Climate, 31, 671–691, doi: 10.1175/JCLI-D-17-0121.1.CrossRefGoogle Scholar
  64. Ek, M. B., K. E. Mitchell, Y. Lin, et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res. Atmos., 108, 8851, doi: 10.1029/2002JD00 3296.CrossRefGoogle Scholar
  65. Entin, J. K., A. Robock, K. Y. Vinnikov, et al., 2000: Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res. Atmos., 105, 11865–11877, doi: 10.1029/2000JD900051.CrossRefGoogle Scholar
  66. Fan, Y. R., G. H. Huang, B. W. Baetz, et al., 2017: Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods. J. Hydrol., 550, 412–426, doi: 10.1016/j.jhy-drol.2017.05.010.CrossRefGoogle Scholar
  67. Fang, L., X. W. Zhan, C. R. Hain, et al., 2018: Impact of using near real-time green vegetation fraction in Noah land surface model of NOAA NCEP on numerical weather predictions. Adv. Meteor., doi: 10.1155/2018/9256396.Google Scholar
  68. Feng, L., J. Li, W. S. Gong, et al., 2016: Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8 OLI images: A solution for large view angle associated problems. Remote Sens. Environ., 174, 56–68, doi: 10.1016/j.rse.2015. 11.031.CrossRefGoogle Scholar
  69. Ferguson, C. R., and D. M. Mocko, 2017: Diagnosing an artificial trend in NLDAS-2 afternoon precipitation. J. Hydrometeor., 18, 1051–1070, doi: 10.1175/JHM-D-16-0251.1.CrossRefGoogle Scholar
  70. Fischer, G., F. Nachtergaele, S. Prieler, et al., 2008: Global Agro-Ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy. Available at http://www.fao.org/soils-portal/soil-survey/soil-maps-and-data-bases/harmonized-world-soil-database-v12/en/. Accessed on 31 March 2019.Google Scholar
  71. Foken, T., 2008: The energy balance closure problem: An overview. Ecol. Appl., 18, 1351–1367, doi: 10.1890/06-0922.1.CrossRefGoogle Scholar
  72. Frei, A., M. Tedesco, S. Lee, et al., 2012: A review of global satellite-derived snow products. Adv. Space Res., 50, 1007–1029, doi: 10.1016/j.asr.2011.12.021.CrossRefGoogle Scholar
  73. Friedl, M. A., D. Sulla-Menashe, B. Tan, et al., 2010: MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168–182, doi: 10.1016/j.rse.2009.08.016.CrossRefGoogle Scholar
  74. Gao, S. G., Z. L. Zhu, H. T. Weng, et al., 2017: Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing. Int. J. Remote Sens., 38, 4782–4803, doi: 10.1080/01431161.2017.1320444.CrossRefGoogle Scholar
  75. Gruber, A., C.–H. Su, W. T. Crow, et al., 2016: Estimating error cross-correlations in soil moisture data sets using extended collocation analysis. J. Geophys. Res. Atmos., 121, 1208–1219, doi: 10.1002/2015JD024027.CrossRefGoogle Scholar
  76. Gupta, H. V., L. A. Bastidas, S. Sorooshian, et al., 1999: Parameter estimation of a land surface scheme using multicriteria methods. J. Geophys. Res. Atmos., 104, 19491–19503, doi: 10.1029/1999JD900154.CrossRefGoogle Scholar
  77. Hamilton, A. S., and R. D. Moore, 2012: Quantifying uncertainty in streamflow records. Can. Water Resour. J., 37, 3–21, doi: 10.4296/cwrj370186.CrossRefGoogle Scholar
  78. Hansen, M. C., R. S. DeFries, J. R. G. Townshend, et al., 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sen., 21, 1331–1364, doi: 10.1080/014311600210209.CrossRefGoogle Scholar
  79. Hao, Z. C., F. H. Hao, Y. L. Xia, et al., 2016a: A statistical method for categorical drought prediction based on NLDAS-2. J. Appl. Meteor. Climatol., 55, 1049–1061, doi: 10.1175/JAMC-D-15-0200.1.CrossRefGoogle Scholar
  80. Hao, Z. C., Y. Hong, Y. L. Xia, et al., 2016b: Probabilistic drought characterization in the categorical form using ordinal regression. J. Hydrol., 535, 331–339, doi: 10.1016/j.jhydrol.2016.01.074.CrossRefGoogle Scholar
  81. Hao, Z. C., X. Yuan, Y. L. Xia, et al., 2017: An overview of drought monitoring and prediction systems at regional and global scales. Bull. Amer. Meteor. Soc., 98, 1879–1896, doi: 10.1175/BAMS-D-15-00149.1.CrossRefGoogle Scholar
  82. Hao, Z. C., V. P. Singh, and Y. L. Xia, 2018: Seasonal drought prediction: Advances, challenges, and future prospects. Rev. Geophys., 56, 108–141, doi: 10.1002/2016RG000549.CrossRefGoogle Scholar
  83. Harmel, R. D., R. J. Cooper, R. M. Slade, et al., 2006: Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Transactions of the ASABE, 49, 689–701, doi: 10.13031/2013.20488.CrossRefGoogle Scholar
  84. Heim, Jr. R. R., 2002: A review of twentieth-century drought indices used in the United States. Bull. Amer. Meteor. Soc., 83, 1149–1166, doi: 10.1175/1520-0477-83.8.1149.CrossRefGoogle Scholar
  85. Henderson-Sellers, A., A. J. Pitman, P. K. Love, et al., 1995: The project for intercomparison of land surface parameterization schemes (PILPS): Phases 2 and 3. Bull. Amer. Meteor. Soc., 76, 489–504, doi: 10.1175/1520-0477(1995)076<0489:TPFI-OL>2.0.CO;2.CrossRefGoogle Scholar
  86. Henry, F., D. E. Herwindiati, S. Mulyono, et al., 2017: Sugarcane land classification with satellite imagery using logistic regression model. IOP Conference Series: Materials Science and Engineering, 185, 012024, doi: 10.1088/1757-899X/185/1/012 024.CrossRefGoogle Scholar
  87. Hersbach, H., and D. Dee, 2016: ERA5 reanalysis is in production. ECMWF Newsletter, 147, 1–7.Google Scholar
  88. Hu, Q., and S. Feng, 2003: A daily soil temperature dataset and soil temperature climatology of the contiguous United States. J. Appl. Meteor., 42, 1139–1156, doi: 10.1175/1520-0450(2003)042<1139:ADSTDA>2.0.CO;2.CrossRefGoogle Scholar
  89. Hu, Q., S. Feng, and G. Schaefer, 2002: Quality control for USDA NRCS SM-ST network soil temperatures: A method and a dataset. J. Appl. Meteor., 41, 607–619, doi: 10.1175/1520-0450(2002)041<0607:QCFUNS>2.0.CO;2.CrossRefGoogle Scholar
  90. Jacobs, C. M. J., E. J. Moors, H. W. Ter Maat, et al., 2008: Evaluation of European Land Data Assimilation System (ELDAS) products using in situ observations. Tellus A, 60, 1023–1037, doi: 10.1111/j.1600-0870.2008.00351.x.CrossRefGoogle Scholar
  91. Jiménez, C., C. Prigent, B. Mueller, et al., 2011: Global intercom-parison of 12 land surface heat flux estimates. J. Geophys. Res. Atmos., 116, D02102, doi: 10.1029/2010JD014545.CrossRefGoogle Scholar
  92. Jiménez-Muñoz, J. C., and J. A. Sobrino, 2006: Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data. Int. J. Remote Sens., 27, 999–1014, doi: 10.1080/01431160500075907.CrossRefGoogle Scholar
  93. Jin, X. L., Z. H. Li, G. J. Yang, et al., 2017: Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS J. Photogram. Remote Sens., 126, 24–37, doi: 10.1016/j.isprsjprs.2017.02.001.CrossRefGoogle Scholar
  94. Jones, J. W., G. Hoogenboom, C. H. Porter, et al., 2003: The DSSAT cropping system model. Eur. J. Agron., 18, 235–265, doi: 10.1016/S1161-0301(02)00107-7.CrossRefGoogle Scholar
  95. Jung, M., M. Reichstein, and A. Bondeau, 2009: Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 2001–2013, doi: 10.5194/bg-6-2001-2009.Google Scholar
  96. Jung, M., M. Reichstein, P. Ciais, et al., 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951–954, doi: 10.1038/nature09396.CrossRefGoogle Scholar
  97. Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472, doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.CrossRefGoogle Scholar
  98. Kanamitsu, M., W. Ebisuzaki, J. Woollen, et al., 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1644, doi: 10.1175/BAMS-83-11-1631.CrossRefGoogle Scholar
  99. Kang, J., R. Jin, X. Li, et al., 2018: Spatial upscaling of sparse soil moisture observations based on ridge regression. Remote Sens., 10, 192, doi: 10.3390/rs10020192.CrossRefGoogle Scholar
  100. Kato, S., F. G. Rose, D. A. Rutan, et al., 2018: Surface irradiances of edition 4.0 clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) data product. J. Climate, 31, 4501–4527, doi: 10.1175/JCLI-D-17-0523.1.CrossRefGoogle Scholar
  101. Kerr, Y. H., 2007: Soil moisture from space: Where are we? Hydrogeol. J., 15, 117–120, doi: 10.1007/s10040-006-0095-3.CrossRefGoogle Scholar
  102. Khaki, M., F. Hamilton, E. Forootan, et al., 2018: Nonparametric data assimilation scheme for land hydrological applications. Water Resour. Res., 54, 4946–4964, doi: 10.1029/2018WR 022854.CrossRefGoogle Scholar
  103. Kitanidis, P. K., and R. L. Bras, 1980: Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resour. Res., 16, 1034–1044, doi: 10.1029/WR016i006p01034.CrossRefGoogle Scholar
  104. Kobayashi, S., Y. Ota, Y. Harada, et al., 2015: The JRA-55 reana-lysis: General specifications and basic characteristics. J. Meteor. Soc. Japan Ser. II, 93, 5–48, doi: 10.2151/jmsj.2015-001.CrossRefGoogle Scholar
  105. Komma, J., G. Blöschl, and C. Reszler, 2008: Soil moisture updating by ensemble Kalman filtering in real-time flood forecasting. J. Hydrol., 357, 228–242, doi: 10.1016/j.jhydrol.2008. 05.020.CrossRefGoogle Scholar
  106. Konzelmann, T., D. R. Cahoon, and C. H. Whitlock, 1996: Impact of biomass burning in equatorial Africa on the downward surface shortwave irradiance: Observations versus calculations. J. Geophys. Res. Atmos., 101, 22833–22844, doi: 10.1029/ 96JD01556.CrossRefGoogle Scholar
  107. Koster, R. D., and M. J. Suarez, 1994: The components of a ‘SVAT’ scheme and their effects on a GCM’s hydrological cycle. Adv. Water Resour., 17, 61–78, doi: 10.1016/0309-1708(94)90024-8.CrossRefGoogle Scholar
  108. Koster, R. D., M. J. Suarez, A. Ducharne, et al., 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res. Atmos., 105, 24809–24822, doi: 10.1029/2000JD900327.CrossRefGoogle Scholar
  109. Kumar, S. V., C. D. Peters-Lidard, Y. Tian, et al., 2006: Land Information System—An interoperable framework for high resolution land surface modeling. Environ. Model. Soft., 21, 1402–1415, doi: 10.1016/j.envsoft.2005.07.004.CrossRefGoogle Scholar
  110. Kumar, S. V., R. H. Reichle, R. D. Koster, et al., 2009: Role of subsurface physics in the assimilation of surface soil moisture observations. J. Hydrometeor., 10, 1534–1547, doi: 10.1175/2009JHM1134.1.CrossRefGoogle Scholar
  111. Kumar, S. V., C. D. Peters-Lidard, D. Mocko, et al., 2014: Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. J. Hydrometeor., 15, 2446–2469, doi: 10.1175/JHM-D-13-0132.1.CrossRefGoogle Scholar
  112. Kumar, S. V., M. Jasinski, D. Mocko, et al., 2018: NCA-LDAS land analysis: Development and performance of a multi-sensor, multivariate land data assimilation system for the National Climate Assessment. J. Hydrometeor., doi: 10.1175/ JHM-D-17-0125.1.Google Scholar
  113. Lahoz, W. A., and P. Schneider, 2014: Data assimilation: Making sense of earth observation. Front. Environ. Sci., 2, 16, doi: 10.3389/fenvs.2014.00016.CrossRefGoogle Scholar
  114. Laloyaux, P., M. Balmaseda, D. Dee, et al., 2016: A coupled data assimilation system for climate reanalysis. Quart. J. Roy. Meteor. Soc., 142, 65–78, doi: 10.1002/qj.2629.CrossRefGoogle Scholar
  115. Lawrence, D. M., K. W. Oleson, M. G. Flanner, et al., 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001, doi: 10.1029/2011MS00045.Google Scholar
  116. Lawston, P. M., J. A. Santanello, Jr. B. F. Zaitchik, et al., 2015: Impact of irrigation methods on land surface model spinup and initialization of WRF forecasts. J. Hydrometeor., 16, 1135–1154, doi: 10.1175/JHM-D-14-0203.1.CrossRefGoogle Scholar
  117. Lee, D. E., and M. Biasutti, 2014: Climatology and variability of precipitation in the twentieth-century reanalysis. J. Climate, 27, 5964–5981, doi: 10.1175/JCLI-D-13-00630.1.CrossRefGoogle Scholar
  118. Leng, G. Y., M. Y. Huang, Q. H. Tang, et al., 2013: Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. J. Geophys. Res. Atmos., 118, 9789–9803, doi: 10.1002/jgrd.50792.CrossRefGoogle Scholar
  119. Leng, G. Y., M. Y. Huang, Q. H. Tang, et al., 2015: A modeling study of irrigation effects on global surface water and ground-water resources under a changing climate. J. Adv. Model. Earth Syst., 7, 1285–1304, doi: 10.1002/2015MS000437.CrossRefGoogle Scholar
  120. Lewis, P., J. Gómez-Dans, T. Kaminski, et al., 2012: An earth observation land data assimilation system (EO-LDAS). Remote Sens. Environ., 120, 219–235, doi: 10.1016/j.rse.2011.12.027.CrossRefGoogle Scholar
  121. Li, R., C. J. Li, Y. Y. Dong, et al., 2011: Assimilation of remote sensing and crop model for LAI estimation based on ensemble Kalman filter. Agric. Sci. China, 10, 1595–1602, doi: 10.1016/S1671-2927(11)60156-9.CrossRefGoogle Scholar
  122. Li, X., C. L. Huang, C. Tao, et al., 2007: Development of a Chinese land data assimilation system: Its progress and prospects. Prog. Natural Sci., 17, 163–173. (in Chinese)CrossRefGoogle Scholar
  123. Li, X., S. M. Liu, H. X. Li, et al., 2018: Intercomparison of six up-scaling evapotranspiration methods: From site to the satellite pixel. J. Geophys. Res. Atmos., 123, 6777–6803, doi: 10.1029/ 2018JD028422.CrossRefGoogle Scholar
  124. Li, Y., Q. G. Zhou, J. Zhou, et al., 2014: Assimilating remote sensing information into a coupled hydrology–crop growth model to estimate regional maize yield in arid regions. Ecological Modelling, 291, 15–27, doi: 10.1016/j.ecolmodel.2014.07.013.CrossRefGoogle Scholar
  125. Li, Z. L., B. H. Tang, H. Wu, et al., 2013: Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ., 131, 14–37, doi: 10.1016/j.rse.2012.12.008.CrossRefGoogle Scholar
  126. Liang, S. L., K. C. Wang, X. T. Zhou, et al., 2010: Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 225–240, doi: 10.1109/JSTARS.2010.2048556.CrossRefGoogle Scholar
  127. Liang, X., D. P. Lettenmaier, E. F. Wood, et al., 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos., 99, 14415–14428, doi: 10.1029/94JD00483.CrossRefGoogle Scholar
  128. Liao, W. L., D. G. Wang, G. L. Wang, et al., 2019: Quality control and evaluation of the observed daily data in North American Soil Moisture Database. J. Meteor. Res., 33,, doi: 10.1007/s13351-019-8121-2.Google Scholar
  129. Lim, Y.-J., K.-Y. Byun, T.-Y. Lee, et al., 2012: A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia. Asia–Pacific J. Atmos. Sci., 48, 83–95, doi: 10.1007/s13143-012-0008-4.CrossRefGoogle Scholar
  130. Liou, Y.-A., and S. K. Kar, 2014: Evapotranspiration estimation with remote sensing and various surface energy balance al-gorithms—A review. Energies, 7, 2821–2849, doi: 10.3390/en7052821.CrossRefGoogle Scholar
  131. Liu, S. M., Z. W. Xu, L. S. Song, et al., 2016: Upscaling evapo-transpiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agric. Forest Meteor., 230–231, 97–113, doi: 10.1016/j.agrformet.2016.04.008.CrossRefGoogle Scholar
  132. Liu, X. M., T. T. Yang, K. Hsu, et al., 2017: Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau. Hydrol. Earth Syst. Sci., 21, 169–181, doi: 10.5194/hess-21-169-2017.CrossRefGoogle Scholar
  133. Liu, Y., A. H. Weerts, M. Clark, et al., 2012: Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci., 16, 3863–3887, doi: 10.5194/hess-16-3863-2012.CrossRefGoogle Scholar
  134. Livneh, B., Y. L. Xia, K. E. Mitchell, et al., 2010: Noah LSM snow model diagnostics and enhancements. J. Hydrometeor., 11, 721–738, doi: 10.1175/2009JHM1174.1.CrossRefGoogle Scholar
  135. Lohmann, D., K. E. Mitchell, P. R. Houser, et al., 2004: Stream-flow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project. J. Geophys. Res. Atmos., 109, D07S91, doi: 10.1029/2003JD003517.CrossRefGoogle Scholar
  136. Luo, L. F., A. Robock, K. E. Mitchell, et al., 2003: Validation of the North American Land Data Assimilation System (NL-DAS) retrospective forcing over the southern Great Plains. J. Geophys. Res. Atmos., 108, 8843, doi: 10.1029/2002JD00 3246.Google Scholar
  137. Ma, Y. P., S. L. Wang, L. Zhang, et al., 2008: Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. Int. J. Appl. Earth Obs. Geoinfo., 10, 426–437, doi: 10.1016/j.jag.2007.09.002.CrossRefGoogle Scholar
  138. Machwitz, M., L. Giustarini, C. Bossung, et al., 2014: Enhanced biomass prediction by assimilating satellite data into a crop growth model. Environ. Model. Soft., 62, 437–453, doi: 10.1016/j.envsoft.2014.08.010.CrossRefGoogle Scholar
  139. Mahfouf, J. F., 2010: Assimilation of satellite-derived soil moisture from ASCAT in a limited-area NWP model. Quart. J. Roy. Meteor. Soc., 136, 784–798, doi: 10.1002/qj.602.Google Scholar
  140. Martens, B., D. G. Miralles, H. Lievens, et al., 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 1903–1925, doi: 10.5194/gmd-10-1903-2017.CrossRefGoogle Scholar
  141. McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Am. Meteor. Soc., Anaheim, CA, USA, 179–184.Google Scholar
  142. McNally, A., K. Arsenault, S. Kumar, et al., 2017: A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4, 170012, doi: 10.1038/sdata.2017.12.CrossRefGoogle Scholar
  143. Meng, J., R. Q. Yang, H. L. Wei, et al., 2012: The land surface analysis in the NCEP climate forecast system reanalysis. J. Hydrometeor., 13, 1621–1630, doi: 10.1175/JHM-D-11-090.1.CrossRefGoogle Scholar
  144. Mesinger, F., G. DiMego, E. Kalnay, et al., 2006: North American regional reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi: 10.1175/BAMS-87-3-343.CrossRefGoogle Scholar
  145. Miller, D. A., and R. A. White, 1998: A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interaction, 2, 1–26, doi: 10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2CrossRefGoogle Scholar
  146. Milly, P. C. D., S. L. Malyshev, E. Shevliakova, et al., 2014: An enhanced model of land water and energy for global hydrolo-gic and earth-system studies. J. Hydrometeor., 15, 1739–1761, doi: 10.1175/JHM-D-13-0162.1.CrossRefGoogle Scholar
  147. Miralles, D. G., T. R. H. Holmes, R. A. M. de Jeu, et al., 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453–469, doi: 10.5194/hess-15-453-2011.CrossRefGoogle Scholar
  148. Mitchell, K., P. Houser, E. Wood, et al., 1999: GCIP Land Data Assimilation System (LDAS) Project now underway. GEWEX News, 9, 3–6.Google Scholar
  149. Mitchell, K. E., D. Lohmann, P. R. Houser, et al., 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geo-phys. Res. Atmos., 109, D07S90, doi: 10.1029/2003JD003823.Google Scholar
  150. Mizukami, N., M. P. Clark, E. D. Gutmann, et al., 2016: Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models. J. Hy-drometeor., 17, 73–98, doi: 10.1175/JHM-D-14-0187.1.Google Scholar
  151. Mizukami, N., M. P. Clark, A. J. Newman, et al., 2017: Towards seamless large-domain parameter estimation for hydrologic models. Water Resour. Res., 53, 8020–8040, doi: 10.1002/2017WR020401.CrossRefGoogle Scholar
  152. Mo, K. C., L. C. Chen, S. Shukla, et al., 2012: Uncertainties in North American land data assimilation systems over the contiguous United States. J. Hydrometeor., 13, 996–1009, doi: 10.1175/JHM-D-11-0132.1.CrossRefGoogle Scholar
  153. Mokhtari, A., H. Noory, and M. Vazifedoust, 2018: Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model. Agric. Forest Meteor., 250–251, 159–170, doi: 10.1016/j.agr-formet.2017.12.250.CrossRefGoogle Scholar
  154. Mu, Q. Z., M. S. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 1781–1800, doi: 10.1016/j.rse.2011.02.019.CrossRefGoogle Scholar
  155. Mu, Q. Z., M. S. Zhao, J. S. Kimball, et al., 2013: A remotely sensed global terrestrial drought severity index. Bull. Amer. Meteor. Soc., 94, 83–98, doi: 10.1175/BAMS-D-11-00213.1.CrossRefGoogle Scholar
  156. Munier, S., A. Polebistki, C. Brown, et al., 2015: SWOT data assimilation for operational reservoir management on the upper Niger River basin. Water Resour. Res., 51, 554–575, doi: 10.1002/2014WR016157.CrossRefGoogle Scholar
  157. Nearing, G. S., D. M. Mocko, C. D. Peters-Lidard, et al., 2016: Benchmarking NLDAS-2 soil moisture and evapotranspira-tion to separate uncertainty contributions. J. Hydrometeor., 17, 745–759, doi: 10.1175/JHM-D-15-0063.1.CrossRefGoogle Scholar
  158. Nijssen, B., S. Shukla, C. Y. Lin, et al., 2014: A prototype Global Drought Information System based on multiple land surface models. J. Hydrometeor., 15, 1661–1676, doi: 10.1175/JHM-D-13-090.1.CrossRefGoogle Scholar
  159. Niu, G. Y., Z. L. Yang, K. E. Mitchell, et al., 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. Atmos., 116, D12109, doi: 10.1029/2010JD015139.CrossRefGoogle Scholar
  160. Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536–549, doi: 10.1175/1520-0493(1989)117<0536: ASPOLS>2.0.CO;2.CrossRefGoogle Scholar
  161. Nouvellon, Y., M. S. Moran, D. Lo Seen, et al., 2001: Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sens. Environ., 78, 131–149, doi: 10.1016/S0034-4257(01)00255-3.CrossRefGoogle Scholar
  162. Novick, K. A., J. A. Biederman, A. R. Desai, et al., 2018: The AmeriFlux network: A coalition of the willing. Agric. Forest Meteor., 249, 444–456, doi: 10.1016/j.agrformet.2017.10.009.CrossRefGoogle Scholar
  163. Oleson, K. W., G.-Y. Niu, Z.-L. Yang, et al., 2008: Improvements to the Community Land Model and their impact on the hydro-logical cycle. J. Geophys. Res. Biogeo., 113, G01021, doi: 10.1029/2007JG000563.CrossRefGoogle Scholar
  164. Onogi, K., J. Tsutsui, H. Koide, et al., 2007: The JRA-25 reanaly-sis. J. Meteor. Soc. Japan Ser. II, 85, 369–432, doi: 10. 2151/jmsj.85.369.CrossRefGoogle Scholar
  165. Osuri, K. K., R. Nadimpalli, U. C. Mohanty, et al., 2017: Improved prediction of severe thunderstorms over the Indian monsoon region using high-resolution soil moisture and temperature initialization. Scientific Reports, 7, 41377, doi: 10.1038/srep41377.CrossRefGoogle Scholar
  166. Palmer, W. C., 1965: Meteorological Drought. Research Paper No. 45, U.S. Weather Bureau, Washington, D. C., 58 pp.Google Scholar
  167. Pan, M., J. Sheffield, E. F. Wood, et al., 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res. Atmos., 108, 8850, doi: 10.1029/2003JD003994.Google Scholar
  168. Parastatidis, D., Z. Mitraka, N. Chrysoulakis, et al., 2017: Online global land surface temperature estimation from landsat. Remote Sens., 9, 1208, doi: 10.3390/rs9121208.CrossRefGoogle Scholar
  169. Pellenq, J., and G. Boulet, 2004: A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface. Agro-nomie, 24, 197–204, doi: 10.1051/agro:2004017.CrossRefGoogle Scholar
  170. Peng, J., A. Loew, O. Merlin, et al., 2017: A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys., 55, 341–366, doi: 10.1002/2016RG000543.CrossRefGoogle Scholar
  171. Penny, S. G., and T. M. Hamill, 2017: Coupled data assimilation for integrated earth system analysis and prediction. Bull. Amer. Meteor. Soc., 98, ES169–ES172, doi: 10.1175/BAMS-D-17-0036.1.CrossRefGoogle Scholar
  172. Penny, S. G., S. Akella, O. Alves, et al., 2017: Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges and Recommendations. World Weather Research Programme (WWRP 2017–3), World Meteorological Organization, Geneva, Switzerland, 59 pp.CrossRefGoogle Scholar
  173. Pinker, R. T., J. D. Tarpley, I. Laszlo, et al., 2003: Surface radiation budgets in support of the GEWEX continental-scale international project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American land data assimilation system (NLDAS) project. J. Geophys. Res. At-mos., 108, 8844, doi: 10.1029/2002JD003301.Google Scholar
  174. Qin, J., K. Yang, N. Lu, et al., 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sens. Environ., 138, 1–9, doi: 10.1016/j.rse.2013.07.003.CrossRefGoogle Scholar
  175. Qin, J., L. Zhao, Y. Y. Chen, et al., 2015: Inter-comparison of spatial upscaling methods for evaluation of satellite-based soil moisture. J. Hydrol., 523, 170–178, doi: 10.1016/j.jhydrol.2015.01.061.CrossRefGoogle Scholar
  176. Quiring, S. M., T. W. Ford, J. K. Wang, et al., 2016: The North American soil moisture database: Development and applications. Bull. Amer. Meteor. Soc., 97, 1441–1459, doi: 10.1175/BAMS-D-13-00263.1.CrossRefGoogle Scholar
  177. Rasmussen, R., B. Baker, J. Kochendorfer, et al., 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811–829, doi: 10.1175/BAMS-D-11-00052.1.CrossRefGoogle Scholar
  178. Reichle, R. H., and R. D. Koster, 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA catchment land surface model. Geophys. Res. Lett., 32, L02404, doi: 10.1029/2004GL021700.CrossRefGoogle Scholar
  179. Reichle, R. H., W. T. Crow, R. D. Koster, et al., 2008: Contribution of soil moisture retrievals to land data assimilation products. Geophys. Res. Lett., 35, L01404, doi: 10.1029/2007 GL031986.CrossRefGoogle Scholar
  180. Reichle, R. H., G. J. M. De Lannoy, Q. Liu, et al., 2017a: Assessment of the SMAP level-4 surface and root-zone soil moisture product using in situ measurements. J. Hydrometeor., 18, 2621–2645, doi: 10.1175/JHM-D-17-0063.1.CrossRefGoogle Scholar
  181. Reichle, R., Q. Liu, R. D. Koster, et al., 2017b: Land surface precipitation in MERRA-2. J. Climate, 30, 1643–1664, doi: 10.1175/JCLI-D-16-0570.1.CrossRefGoogle Scholar
  182. Rennie, J. J., J. H. Lawrimore, B. E. Gleason, et al., 2014: The international surface temperature initiative global land surface databank: Monthly temperature data release description and methods. Geosci. Data J., 1, 75–102, doi: 10.1002/gdj3.8.CrossRefGoogle Scholar
  183. Reynolds, C. A., T. J. Jackson, and W. J. Rawls, 2000: Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions. Water Re-sour. Res., 36, 3653–3662, doi: 10.1029/2000WR900130.CrossRefGoogle Scholar
  184. Rienecker, M. M., M. J. Suarez, R. Gelaro, et al., 2011: MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Climate, 24, 3624–3648, doi: 10.1175/JCLI-D-11-00015.1.CrossRefGoogle Scholar
  185. Robock, A., L. F. Luo, E. F. Wood, et al., 2003: Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season. J. Geophys. Res. Atmos., 108, 8846, doi: 10.1029/2002JD003245.Google Scholar
  186. Rodell, M., P. R. Houser, U. Jambor, et al., 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381–394, doi: 10.1175/BAMS-85-3-381.CrossRefGoogle Scholar
  187. Saha, S., S. Moorthi, H.-L. Pan, et al., 2010: The NCEP climate forecast system reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1058, doi: 10.1175/2010BAMS3001.1.CrossRefGoogle Scholar
  188. Saha, S., S. Moorthi, X. R. Wu, et al, 2014: The NCEP climate forecast system version 2. J. Climate, 27, 2185–2208, doi: 10.1175/JCLI-D-12-00823.1.CrossRefGoogle Scholar
  189. Santanello, Jr. J. A., S. V. Kumar, C. D. Peters-Lidard, et al., 2016: Impact of soil moisture assimilation on land surface model spinup and coupled land–atmosphere prediction. J. Hydromet-eor., 17, 517–540, doi: 10.1175/JHM-D-15-0072.1.CrossRefGoogle Scholar
  190. Sawada, Y., and T. Koike, 2016: Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010–2011). J. Geophy. Res. Atmos., 121, 8229–8242, doi: 10.1002/2015JD024705.CrossRefGoogle Scholar
  191. Schaake, J. C., Q. Y. Duan, V. Koren, et al., 2004: An intercom-parison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res. Atmos., 109, D01S90, doi: 10.1029/2002JD003309.CrossRefGoogle Scholar
  192. Schaefer, G. L., M. H. Cosh, and T. J. Jackson, 2007: The USDA natural resources conservation service soil climate analysis network (SCAN). J. Atmos. Oceanic Technol., 24, 2073–2077, doi: 10.1175/2007JTECHA930.1.CrossRefGoogle Scholar
  193. Sellers, P. J., Y. Mintz, Y. C. Sud, et al., 1986: A simple biosphere model (SIB) for use within general circulation models. J. Atmos. Sci., 43, 505–531, doi: 10.1175/1520-0469(1986) 043<0505:ASBMFU>2.0.CO;2.CrossRefGoogle Scholar
  194. Seo, D.-J., Y. Q. Liu, H. Moradkhani, et al., 2014: Ensemble prediction and data assimilation for operational hydrology. J. Hy-drol., 519, 2661–2662, doi: 10.1016/j.jhydrol.2014.11.035.CrossRefGoogle Scholar
  195. Sequera, P., J. E. González, K. McDonald, et al., 2016: Improvements in land-use classification for estimating daytime surface temperatures and sea-breeze flows in Southern California. Earth Interaction, 20, 1–32, doi: 10.1175/EI-D-14-0034.1.CrossRefGoogle Scholar
  196. Sheffield, J., M. Pan, E. F. Wood, et al., 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent. J. Geophys. Res. Atmos., 108, 8849, doi: 10.1029/2002 JD003274.CrossRefGoogle Scholar
  197. Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 3088–3111, doi: 10.1175/JCLI3790.1.CrossRefGoogle Scholar
  198. Shi, C. X., Z. H. Xie, H. Qian, et al., 2011: China land soil moisture EnKF data assimilation based on satellite remote sensing data. Sci. China Earth Sci., 54, 1430–1440, doi: 10.1007/s11430-010-4160-3.CrossRefGoogle Scholar
  199. Shuttleworth, W. J., 2007: Putting the “vap” into evaporation. Hy-drol. Earth Syst. Sci., 11, 210–244, doi: 10.5194/hess-11-210-2007.CrossRefGoogle Scholar
  200. Singh, R. S., J. T. Reager, N. L. Miller, et al., 2015: Toward hyper-resolution land-surface modeling: The effects of fine-scale topography and soil texture on CLM4.0 simulations over the Southwestern U.S. Water Resour. Res., 51, 2648–2667, doi: 10.1002/2014WR015686.CrossRefGoogle Scholar
  201. Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 1870–1884, doi: 10.1175/1520-0493(1997)125<1870:PODSMC>2.0.CO;2.CrossRefGoogle Scholar
  202. Snauffer, A. M., W. W. Hsieh, and A. J. Cannon, 2016: Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. J. Hydrol., 541, 714–726, doi: 10.1016/j.jhydrol.2016.07.027.CrossRefGoogle Scholar
  203. Sun, Q., C. Miao, Q. Duan, et al., 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparis-ons. Rev. Geophys., 56, 79–107, doi: 10.1002/rog.v56.1.CrossRefGoogle Scholar
  204. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos., 106, 7183–7192, doi: 10.1029/2000JD900719.CrossRefGoogle Scholar
  205. Troy, T. J., and E. F. Wood, 2009: Comparison and evaluation of gridded radiation products across northern Eurasia. Environ. Res. Lett., 4, 045008, doi: 10.1088/1748-9326/4/4/045008.CrossRefGoogle Scholar
  206. Troy, T. J., E. F. Wood, and J. Sheffield, 2008: An efficient calibration method for continental-scale land surface modeling. Water Resour. Res., 44, W09411, doi: 10.1029/2007WR006513.CrossRefGoogle Scholar
  207. Ungersböck, M., F. Rubel, T. Fuchs, et al., 2001: Bias correction of global daily rain gauge measurements. Phys. Chem. Earth B: Hydrol., Oceans Atmos., 26, 411–414, doi: 10.1016/S14 64-1909(01)00027-2.CrossRefGoogle Scholar
  208. Uppala, S. M., P. W. KÅllberg, A. J. Simmons, et al., 2005: The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi: 10.1256/qj.04.176.CrossRefGoogle Scholar
  209. van den Hurk, B. J. J. M., P. Viterbo, A. C. M. Beljaars, et al., 2000: Offline Validation of the ERA-40 Surface Scheme. ECMWF Tech. Memo., 295, European Center for Medium-Range Weather Forecasts, Reading, UK, 43 pp.Google Scholar
  210. van Diepen, C. A., J. Wolf, H. van Keulen, et al., 1989: WOFOST: A simulation model of crop production. Soil Use Manag., 5, 16–24, doi: 10.1111/j.1475-2743.1989.tb00755.x.CrossRefGoogle Scholar
  211. Wagner, W., G. Blöschl, P. Pampaloni, et al., 2007: Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Hydrol. Res., 38, 1–20, doi: 10.2166/nh.2007.029.CrossRefGoogle Scholar
  212. Wan, Z. M., 2014: New refinements and validation of the collec-tion-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ., 40, 36–45, doi: 10.1016/j.rse.2013.08.027.CrossRefGoogle Scholar
  213. Wang, A. H., and X. B. Zeng, 2013: Development of global hourly 0.5° land surface air temperature datasets. J. Climate, 26, 7676–7691, doi: 10.1175/JCLI-D-12-00682.1.CrossRefGoogle Scholar
  214. Wang, S., B. C. Ancell, G. H. Huang, et al., 2018: Improving robustness of hydrologic ensemble predictions through probabilistic pre- and post-processing in sequential data assimilation. Water Resour. Res., 54, 2129–2151, doi: 10.1002/2018WR022546.CrossRefGoogle Scholar
  215. Wang, W., W. Cui, X. J. Wang, et al., 2016: Evaluation of GL-DAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. J. Hydrometeor., 17, 2815–2833, doi: 10.1175/JHM-D-15-0191.1.CrossRefGoogle Scholar
  216. Wei, H. L., Y. L. Xia, K. E. Mitchell, et al., 2013: Improvement of the Noah land surface model for warm season processes: Evaluation of water and energy flux simulation. Hydrol. Process., 27, 297–303, doi: 10.1002/hyp.9214.CrossRefGoogle Scholar
  217. Wei, S. G., Y. J. Dai, Q. Y. Duan, et al., 2014: A global soil data set for earth system modeling. J. Adv. Model. Earth Syst., 6, 249–263, doi: 10.1002/2013MS000293.CrossRefGoogle Scholar
  218. Wilson, K., A. Goldstein, E. Falge, et al., 2002: Energy balance closure at FLUXNET sites. Agric. Forest Meteor., 113, 223–243, doi: 10.1016/S0168-1923(02)00109-0.CrossRefGoogle Scholar
  219. Wood, E. F., J. K. Roundy, T. J. Try, et al., 2011: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour. Res., 47, W05301, doi: 10.1029/2010WR010090.CrossRefGoogle Scholar
  220. Xia, Y. L., A. J. Pitman, H. V. Gupta, et al., 2002: Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset. J. Hydrometeor., 3, 181–194, doi: 10.1175/1525-7541(2002)003<0181:CALSMO>2.0.CO;2.CrossRefGoogle Scholar
  221. Xia, Y. L., K. E. Mitchell, M. B. Ek, et al., 2012a: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res. Atmos., 117, D03109, doi: 10.1029/2011JD016048.Google Scholar
  222. Xia, Y. L., K. E. Mitchell, M. B. Ek, et al., 2012b: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res. Atmos., 117, D03110, doi: 10.1029/2011JD016051.Google Scholar
  223. Xia, Y. L., B. A. Cosgrove, M. B. Ek, et al., 2013a: Overview of the North American Land Data Assimilation System (NL-DAS). Land Surface Observation, Modeling and Data Assimilation, S. L. Liang, X. Li, and X. H. Xie, Eds., World Scientific, Hackensack NJ, 337–377.CrossRefGoogle Scholar
  224. Xia, Y. L., M. B. Ek, J. Sheffield, et al., 2013b: Validation of Noah-simulated soil temperature in the North American Land Data Assimilation System phase 2. J. Appl. Meteor. Climatol., 52, 455–471, doi: 10.1175/JAMC-D-12-033.1.CrossRefGoogle Scholar
  225. Xia, Y. L., M. B. Ek, D. Mocko, et al., 2014a: Uncertainties, correlations, and optimal blends of drought indices from the NL-DAS multiple land surface model ensemble. J. Hydrometeor., 15, 1636–1650, doi: 10.1175/JHM-D-13-058.1.CrossRefGoogle Scholar
  226. Xia, Y. L., M. B. Ek, C. D. Peters-Lidard, et al., 2014b: Application of USDM statistics in NLDAS-2: Optimal blended NL-DAS drought index over the continental United States. J. Geophys. Res. Atmos., 119, 2947–2965, doi: 10.1002/2013 JD020994.CrossRefGoogle Scholar
  227. Xia, Y. L., M. T. Hobbins, Q. Z. Mu, et al., 2015a: Evaluation of NLDAS-2 evapotranspiration against tower flux site observations. Hydrol. Process., 29, 1757–1771, doi: 10.1002/hyp.10299.CrossRefGoogle Scholar
  228. Xia, Y. L., M. B. Ek, Y. H. Wu, et al., 2015b: Comparison of NL-DAS-2 simulated and NASMD observed daily soil moisture. Part I: Comparison and analysis. J. Hydrometeor., 16, 1962–1980, doi: 10.1175/JHM-D-14-0096.1.CrossRefGoogle Scholar
  229. Xia, Y. L., D. M. Mocko, M. Huang, et al., 2017: Comparison and assessment of three advanced land surface models in simulating terrestrial water storage components over the United States. J. Hydrometeor., 18, 625–649, doi: 10.1175/JHM-D-16-0112.1.CrossRefGoogle Scholar
  230. Xia, Y. L., D. M. Mocko, S. G. Wang, et al., 2018: Comprehensive evaluation of the variable infiltration capacity (VIC) model in the North American Land Data Assimilation System. J. Hydrometeor., 17, 1853–1879, doi: 10.1175/JHM-D-18-0139.1.CrossRefGoogle Scholar
  231. Xiao, J. F., J. Q. Chen, K. J. Davis, et al., 2012: Advances in up-scaling of eddy covariance measurements of carbon and water fluxes. J. Geophys. Res. Biogeo., 117, G00J01, doi: 10.1029/2011JG001889.CrossRefGoogle Scholar
  232. Xie, Y., P. X. Wang, X. J. Bai, et al., 2017: Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model. Agric. Forest Meteor., 246, 194–206, doi: 10.1016/j.agrformet.2017.06.015.CrossRefGoogle Scholar
  233. Xu, T. R., S. L. Liang, and S. M. Liu, 2011: Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter. J. Geophys. Res. Atmos., 116, D09109, doi: 10.1029/2010JD 015150.Google Scholar
  234. Xu, T. R., S. M. Liu, Z. W. Xu, et al., 2015: A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature. Sci. China Earth Sci., 58, 211–230, doi: 10.1007/s11430-014-4964-7.CrossRefGoogle Scholar
  235. Xu, T. R., Z. X. Guo, S. M. Liu, et al., 2018: Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. J. Geophysics. Res. Atmos., 123, 8674–8690, doi: 10.1029/2018JD028447.CrossRefGoogle Scholar
  236. Xu, T. R., X. L. He, S. M. Bateni, et al., 2019: Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites. Remote Sens. Environ., 221, 444–461, doi: 10.1016/j.rse.2018.11.023.CrossRefGoogle Scholar
  237. Yang, D. Q., B. E. Goodison, J. R. Metcalfe, et al., 1998: Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 54–68, doi: 10.1175/1520-0426(1998) 015<0054:AONSNP>2.0.CO;2.CrossRefGoogle Scholar
  238. Yang, D. Q., D. Kane, Z. P. Zhang, et al., 2005: Bias corrections of long-term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501, doi: 10.1029/2005GL024057.Google Scholar
  239. Yang, F., H. Lu, K. Yang, et al., 2017: Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China. Hydrol. Earth Syst. Sci., 21, 5805–5821, doi: 10.5194/hess-21-5805-2017.CrossRefGoogle Scholar
  240. Yang, K., T. Watanabe, T. Koike, et al., 2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget. J. Meteor. Soc. Japan Ser. II, 85, 229–242.CrossRefGoogle Scholar
  241. Yang, K., T. Koike, I. Kaihotsu, et al., 2009: Validation of a dual-pass microwave land data assimilation system for estimating surface soil moisture in semiarid regions. J. Hydrometeor., 10, 780–793, doi: 10.1175/2008JHM1065.1.CrossRefGoogle Scholar
  242. Yang, K., L. Zhu, Y. Y. Chen, et al., 2016: Land surface model calibration through microwave data assimilation for improving soil moisture simulations. J. Hydrol., 523, 266–276, doi: 10.1016/j.jhydrol.2015.12.018.CrossRefGoogle Scholar
  243. Yang, R. Q., K. Mitchell, J. Meng, et al., 2011: Summer-season forecast experiments with the NCEP Climate Forecast System using different land models and different initial land states. J. Climate, 24, 2319–2334, doi: 10.1175/2010JCLI 3797.1.CrossRefGoogle Scholar
  244. Yilmaz, M. T., W. T. Crow, M. C. Anderson, et al., 2012: An objective methodology for merging satellite- and model-based soil moisture products. Water Resour. Res., 48, W11502, doi: 10.1029/2011WR011682.CrossRefGoogle Scholar
  245. Yu, Y. Y., D. Tarpley, J. L. Privette, et al., 2009: Developing algorithm for operational GOES-R land surface temperature product. IEEE Trans. Geosci. Remote Sens., 47, 936–951, doi: 10.1109/TGRS.2008.2006180.CrossRefGoogle Scholar
  246. Yuan, X., P. Ji, L. Y. Wang, et al., 2018: High-resolution land surface modeling of hydrological changes over the Sanjiangy-uan region in the eastern Tibetan Plateau: 1. Model development and evaluation. J. Adv. Model. Earth Syst., 10, 2806–2828, doi: 10.1029/2018MS001412.CrossRefGoogle Scholar
  247. Zaitchik, B. F., M. Rodell, and F. Olivera, 2010: Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme. Water Re-sour. Res., 46, W06507, doi: 10.1029/2009WR007811.Google Scholar
  248. Zhang, K., J. S. Kimball, and S. W. Running, 2016: A review of remote sensing based actual evapotranspiration estimation. WIREs Water, 3, 834–853, doi: 10.1002/wat2.1168.CrossRefGoogle Scholar
  249. Zhang, T. P., P. W. Stackhouse, S. K. Gupta, et al., 2013: The validation of the GEWEX SRB surface shortwave flux data products using BSRN measurements: A systematic quality control, production and application approach. J. Quant. Spec-trosc. Radiat. Transfer, 122, 127–140, doi: 10.1016/j.jqsrt.2012.10.004.CrossRefGoogle Scholar
  250. Zhang, T. P., P. W. Stackhouse, J. S. Gupta, et al., 2015: The validation of the GEWEX SRB surface longwave flux data products using BSRN measurements. J. Quant. Spectrosc. Radiat. Transfer, 150, 134–147, doi: 10.1016/j.jqsrt.2014.07.013.CrossRefGoogle Scholar
  251. Zheng, H., and Z. L. Yang, 2016: Effects of soil-type datasets on regional terrestrial water cycle simulations under different climatic regimes. J. Geophys. Res. Atmos., 121, 14,387–14,402, doi: 10.1002/2016JD025187.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Youlong Xia
    • 1
  • Zengchao Hao
    • 2
    Email author
  • Chunxiang Shi
    • 3
  • Yaohui Li
    • 4
  • Jesse Meng
    • 1
  • Tongren Xu
    • 5
  • Xinying Wu
    • 2
  • Baoqing Zhang
    • 6
  1. 1.I.M. Systems Group at Environmental Modeling Center (EMC)National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA)College ParkUSA
  2. 2.College of Water SciencesBeijing Normal UniversityBeijingChina
  3. 3.National Meteorological Information CenterChina Meteorological AdministrationBeijingChina
  4. 4.Institute of Arid MeteorologyChina Meteorological AdministrationLanzhouChina
  5. 5.State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  6. 6.Key Laboratory of Western China’s Environmental Systems of Ministry of Education, College of Earth and Environmental SciencesLanzhou UniversityLanzhouChina

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