[1] 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. Hydrometeorol., 8, 1225–1242. doi: 10.1175/2007JHM819.1
[2] Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1024. doi: 10.1175/BAMS-84-8-1013
[3] Decker, M., and X. B. Zeng, 2009: Impact of modified Richards equation on global soil moisture simulation in the Community Land Model (CLM3.5). J. Adv. Model. Earth Syst., 1, 5. doi: 10.3894/JAMES.2009.1.5
[4] Dumedah, G., and P. Coulibaly, 2013: Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation. Adv. Water Resour., 60, 47–63. doi: 10.1016/j.advwatres.2013.07.007
[5] Dumedah, G., and J. P. Walker, 2014: Assessment of land surface model uncertainty: A crucial step towards the identification of model weaknesses. J. Hydrol., 519, 1474–1484. doi: 10.1016/j.jhydrol.2014.09.015
[6] Dumedah, G., and J. P. Walker, 2017: Assessment of model behavior and acceptable forcing data uncertainty in the context of land surface soil moisture estimation. Adv. Water Resour., 101, 23–36. doi: 10.1016/j.advwatres.2017.01.001
[7] Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans, 99, 10143–10162. doi: 10.1029/94JC00572
[8] Famiglietti, J. S., and E. F. Wood, 1994: Multiscale modeling of spatially variable water and energy balance process. Water Resour. Res., 30, 3061–3078. doi: 10.1029/94WR01498
[9] Fu, X. L., Z. B. Yu, L. F. Luo, et al., 2014: Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter. Stoch. Environ. Res. Risk Assess., 28, 681–693. doi: 10.1007/s00477-013-0781-3
[10] Fu, X. L., L. F. Luo, M. Pan, et al., 2018a: Evaluation of TOPMODEL-based land surface–atmosphere transfer scheme (TOPLATS) through a soil moisture simulation. Earth Interact., 22, 1–19. doi: 10.1175/EI-D-17-0037.1
[11] Fu, X. L., Z. B. Yu, Y. J. Ding, et al., 2018b: Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land mo-del. Water Sci. Eng., 11, 196–204. doi: 10.1016/j.wse.2018.09.003
[12] Han, X. J., and X. Li, 2008: An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation. Remote Sens. Environ., 112, 1434–1449. doi: 10.1016/j.rse.2007.07.008
[13] Han, X. J., H. J. H. Franssen, C. Montzka, et al., 2014: Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations. Water Resour. Res., 50, 6081–6105. doi: 10.1002/2013WR014586
[14] Heathman, G. C., P. J. Starks, L. R. Ahuja, et al., 2003: Assimilation of surface soil moisture to estimate profile soil water content. J. Hydrol., 279, 1–17. doi: 10.1016/S0022-1694(03)00088-X
[15] Heemink, A. W., M. Verlaan, and J. Segers, 2001: Variance reduced ensemble Kalman filtering. Mon. Wea. Rev., 129, 1718–1728. doi: 10.1175/1520-0493(2001)129<1718:VREKF>2.0.CO;2
[16] Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123–137. doi: 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
[17] Huang, C. L., and X. Li, 2004: A review of land data assimilation system. Remote Sens. Technol. Appl., 19, 424–430. (in Chinese) doi: 10.3969/j.issn.1004-0323.2004.05.026
[18] Huang, C. L., X. Li, L. Lu, et al., 2008: Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter. Remote Sens. Environ., 112, 888–900. doi: 10.1016/j.rse.2007.06.026
[19] Jackson, T. J., D. M. Le Vine, A. Y. Hsu, et al., 1999: Soil moisture mapping at regional scales using microwave radiometry: The southern great plains hydrology experiment. IEEE Trans. Geosci. Remote Sens., 37, 2136–2151. doi: 10.1109/36.789610
[20] Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 35–45. doi: 10.1115/1.3662552
[21] Koster, R. D., P. A. Dirmeyer, Z. C. Guo, et al., 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140. doi: 10.1126/science.1100217
[22] Lai, X., J. Wen, S. X. Cen, et al., 2014: Numerical simulation and evaluation study of soil moisture over China by using CLM4.0 model. Chinese J. Atmos. Sci., 38, 499–512. (in Chinese) doi: 10.3878/j.issn.1006-9895.1401.13194
[23] Li, F. Q., W. T. Crow, and W. P. Kustas, 2010: Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Adv. Water Resour., 33, 201–214. doi: 10.1016/j.advwatres.2009.11.007
[24] Liang, X., D. P. Lettennmaier, 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
[25] Lievens, H., G. J. M. De Lannoy, A. Al Bitar, et al., 2016: Assimilation of SMOS soil moisture and brightness temperature products into a land surface model. Remote Sens. Environ., 180, 292–304. doi: 10.1016/j.rse.2015.10.033
[26] Liu, D., A. K. Mishra, and Z. B. Yu, 2016: Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering. J. Hydrol., 538, 243–255. doi: 10.1016/j.jhydrol.2016.04.021
[27] Liu, H. R., F. Y. Lu, Z. Y. Liu, et al., 2016: Assimilating atmosphere reanalysis in coupled data assimilation. J. Meteor. Res., 30, 572–583. doi: 10.1007/s13351-016-6014-1
[28] Luo, L. F., A. Robock, K. E. Mitchell, et al., 2003: Validation of the North American land data assimilation system (NLDAS) retrospective forcing over the southern Great Plains. J. Geophys. Res. Atmos., 108, 8843. doi: 10.1029/2002JD003246
[29] Luo, S. Q., X. W. Fang, S. H. Lyu, et al., 2017: Improving CLM4.5 simulations of land–atmosphere exchange during freeze–thaw processes on the Tibetan Plateau. J. Meteor. Res., 31, 916–930. doi: 10.1007/s13351-017-6063-0
[30] Lyu, H. S., Z. B. Yu, R. Horton, et al., 2011a: Multi-scale assimilation of root zone soil water predictions. Hydrol. Processes, 25, 3158–3172. doi: 10.1002/hyp.8034
[31] Lyu, H. S., Z. B. Yu, Y. H. Zhu, et al., 2011b: Dual state-parame-ter estimation of root zone soil moisture by optimal parame-ter estimation and extended Kalman filter data assimilation. Adv. Water Resour., 34, 395–406. doi: 10.1016/j.advwatres.2010.12.005
[32] Milly, P. C. D., J. Betancourt, M. Falkenmark, et al., 2008: Stationarity is dead: Whither water management? Science, 319, 573–574. doi: 10.1126/science.1151915
[33] Monteith, J. L., 1973: Principles of Environmental Physics. Edward Arnold, London, 242 pp.
[34] Moradkhani, H., S. Sorooshian, H. V. Gupta, et al., 2005: Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour., 28, 135–147. doi: 10.1016/j.advwatres.2004.09.002
[35] Oleson, K. W., Y. J. Dai, G. B. Bonan, et al., 2004: Technical Description of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder, CO, doi: 10.5065/D6N877R0.
[36] 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
[37] Schaake, J. C., Q. Y. Duan, V. Koren, et al., 2004: An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res. Atmos., 109, D01S90. doi: 10.1029/2002JD003309
[38] Sellers, P. J., D. A. Randall, G. J. Collatz, et al., 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Climate, 9, 676–705. doi: 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2
[39] Shi, J. C., L. M. Jiang, L. X. Zhang, et al., 2006: Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Trans. Geosci. Remote Sens., 44, 3145–3153. doi: 10.1109/TGRS.2006.876706
[40] Vrugt, J. A., H. V. Gupta, B. O. Nualláin, et al., 2006: Real-time data assimilation for operational ensemble streamflow forecasting. J. Hydrometeorol., 7, 548–565. doi: 10.1175/JHM504.1
[41] Wang, G. J., D. Chyi, L. Wang, et al., 2016: Soil moisture retrie-val over Northeast China based on microwave brightness temperature of FY3B satellite and its comparison with other datasets. Chinese J. Atmos. Sci., 40, 792–804. (in Chinese) doi: 10.3878/j.issn.1006-9895.1509.15207
[42] Weerts, A. H., and G. Y. H. El Serafy, 2006: Particle filtering and ensemble Kalman filtering for state updating with hydrologi-cal conceptual rainfall-runoff models. Water Resour. Res., 42, W09403. doi: 10.1029/2005WR004093
[43] Western, A. W., and G. Blöschl, 1999: On the spatial scaling of soil moisture. J. Hydrol., 217, 203–224. doi: 10.1016/S0022-1694(98)00232-7
[44] Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924. doi: 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2
[45] Xiang, L., W. W. Ling, Y. S. Zhu, et al., 2016: Self-adaptive Green-Ampt infiltration parameters obtained from measured moisture processes. Water Sci. Eng., 9, 256–264. doi: 10.1016/j.wse.2016.05.001
[46] Xie, X. H., and D. X. Zhang, 2010: Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter. Adv. Water Resour., 33, 678–690. doi: 10.1016/j.advwatres.2010.03.012
[47] Yeh, T. C., R. T. Wetherald, and S. Manabe, 1984: The effect of soil moisture on the short-term climate and hydrology change—A numerical experiment. Mon. Wea. Rev., 112, 474–490. doi: 10.1175/1520-0493(1984)112<0474:TEOSMO>2.0.CO;2
[48] Yu, Z. B., T. N. Carlson, E. J. Barron, et al., 2001: On evaluating the spatial–temporal variation of soil moisture in the Susquehanna River Basin. Water Resour. Res., 37, 1313–1326. doi: 10.1029/2000WR900369
[49] Yu, Z. B, X. L. Fu, L. F. Luo, et al., 2014a: One-dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODISLST with the ensemble particle filter. Water Resour. Res., 50, 6950–6965. doi: 10.1002/2012WR013473
[50] Yu, Z. B., X. L. Fu, H. S. Lyu, et al., 2014b: Evaluating ensemble Kalman, particle, and ensemble particle filters through soil temperature prediction. J. Hydrol. Eng., 19, 0414027. doi: 10.1061/(ASCE)HE.1943-5584.0000976