Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model
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摘要
Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.
Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.
Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.
引文
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.
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.
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.
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.
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.
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.
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.
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.
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.
Fu,X.L.,L.F.Luo,M.Pan,et al.,2018a:Evaluation of TOP-MODEL-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.
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 model.Water Sci.Eng.,11,196-204,doi:10.1016/j.wse.2018.09.003.
Han,X.J.,and X.Li,2008:An evaluation of the nonlinear/nonGaussian filters for the sequential data assimilation.Remote Sens.Environ.,112,1434-1449,doi:10.1016/j.rse.2007.07.008.
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.
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.
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.
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.
Huang,C.L.,and X.Li,2004:A review of land data assimilation system.Remote Sens.Technol.Appl.,19,424-430,doi:10.3969/j.issn.1004-0323.2004.05.026.(in Chinese)
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.
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.
Kalman,R.E.,1960:A new approach to linear filtering and prediction problems.J.Basic Eng.,82,35-45,doi:10.1115/1.3662552.
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.
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,doi:10.3878/j.issn.1006-9895.1401.13194.(in Chinese)
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.
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.
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.
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.
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.
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.
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.
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.
Lyu,H.S.,Z.B.Yu,Y.H.Zhu,et al.,2011b:Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation.Adv.Water Resour.,34,395-406,doi:10.1016/j.advwatres.2010.12.005.
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.
Monteith,J.L.,1973:Principles of Environmental Physics.Edward Arnold,London,242 pp.
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.
Oleson,K.W.,Y.J.Dai,G.B.Bonan,et al.,2004:Technical Description of the Community Land Model(CLM).NCARTechnical Note NCAR/TN-461+STR,National Center for Atmospheric Research,Boulder,CO,doi:10.5065/D6N877R0.
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.
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.
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.
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.
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.
Wang,G.J.,D.Chyi,L.Wang,et al.,2016:Soil moisture retrieval over Northeast China based on microwave brightness temperature of FY3B satellite and its comparison with other datasets.Chinese J.Atmos.Sci.,40,792-804,doi:10.3878/j.issn.1006-9895.1509.15207.(in Chinese)
Weerts,A.H.,and G.Y.H.El Serafy,2006:Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models.Water Resour.Res.,42,W09403,doi:10.1029/2005WR004093.
Western,A.W.,and G.Bl?schl,1999:On the spatial scaling of soil moisture.J.Hydrol.,217,203-224,doi:10.1016/S0022-1694(98)00232-7.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Fu,X.L.,L.F.Luo,M.Pan,et al.,2018a:Evaluation of TOP-MODEL-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.
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 model.Water Sci.Eng.,11,196-204,doi:10.1016/j.wse.2018.09.003.
Han,X.J.,and X.Li,2008:An evaluation of the nonlinear/nonGaussian filters for the sequential data assimilation.Remote Sens.Environ.,112,1434-1449,doi:10.1016/j.rse.2007.07.008.
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.
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.
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.
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.
Huang,C.L.,and X.Li,2004:A review of land data assimilation system.Remote Sens.Technol.Appl.,19,424-430,doi:10.3969/j.issn.1004-0323.2004.05.026.(in Chinese)
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.
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.
Kalman,R.E.,1960:A new approach to linear filtering and prediction problems.J.Basic Eng.,82,35-45,doi:10.1115/1.3662552.
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.
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,doi:10.3878/j.issn.1006-9895.1401.13194.(in Chinese)
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.
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.
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.
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.
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.
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.
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.
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.
Lyu,H.S.,Z.B.Yu,Y.H.Zhu,et al.,2011b:Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation.Adv.Water Resour.,34,395-406,doi:10.1016/j.advwatres.2010.12.005.
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.
Monteith,J.L.,1973:Principles of Environmental Physics.Edward Arnold,London,242 pp.
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.
Oleson,K.W.,Y.J.Dai,G.B.Bonan,et al.,2004:Technical Description of the Community Land Model(CLM).NCARTechnical Note NCAR/TN-461+STR,National Center for Atmospheric Research,Boulder,CO,doi:10.5065/D6N877R0.
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.
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.
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.
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.
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.
Wang,G.J.,D.Chyi,L.Wang,et al.,2016:Soil moisture retrieval over Northeast China based on microwave brightness temperature of FY3B satellite and its comparison with other datasets.Chinese J.Atmos.Sci.,40,792-804,doi:10.3878/j.issn.1006-9895.1509.15207.(in Chinese)
Weerts,A.H.,and G.Y.H.El Serafy,2006:Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models.Water Resour.Res.,42,W09403,doi:10.1029/2005WR004093.
Western,A.W.,and G.Bl?schl,1999:On the spatial scaling of soil moisture.J.Hydrol.,217,203-224,doi:10.1016/S0022-1694(98)00232-7.
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.
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.
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.
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.
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.
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.
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