Land Surface Data Assimilation

  • Paul R. HouserEmail author
  • Gabriëlle J.M. De Lannoy
  • Jeffrey P. Walker


Accurate knowledge of spatial and temporal land surface storages and fluxes are essential for addressing a wide range of important, socially relevant science, education, application and management issues. Improved estimates of land surface conditions are directly applicable to agriculture, ecology, civil engineering, water resources management, rainfall-runoff prediction, atmospheric process studies, climate and weather prediction, and disaster management (Houser et al. 2004).


Soil Moisture Kalman Filter Data Assimilation Brightness Temperature Extend Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Andreadis, K.M. and D.P. Lettenmaier, 2006. Assimilating remotely sensed snow observation into a macroscale hydrology model. Adv. Water Resour., 29, 872–886.Google Scholar
  2. Arya, L.M., J.C. Richter and J.F. Paris, 1983. Estimating profile water storage from surface zone soil moisture measurements under bare field conditions. Water Resour. Res., 19, 403–412.Google Scholar
  3. Aubert, D., C. Loumagne and L. Oudin, 2003. Sequential assimilation of soil moisture and streamflow data into a conceptual rainfall-runoff model. J. Hydrol., 280, 145–161.Google Scholar
  4. Bennett, A.F., 1992. Inverse Methods in Physical Oceanography, Cambridge University Press, Cambridge, 346 pp.Google Scholar
  5. Bergthorsson, P. and B. Döös, 1955. Numerical weather map analysis. Tellus, 7, 329–340.Google Scholar
  6. Bernard, R., M. Vauclin and D. Vidal-Madjar, 1981. Possible use of active microwave remote sensing data for prediction of regional evaporation by numerical simulation of soil water movement in the unsaturated zone. Water Resour. Res., 17, 1603–1610.Google Scholar
  7. Beven, K., 1989. Changing ideas in hydrology: The case of physically-based models. J. Hydrol., 105, 157–172.Google Scholar
  8. Boni, G., D. Entekhabi and F. Castelli, 2001. Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resour. Res., 37, 1713–1722.Google Scholar
  9. Bosilovich, M.G., J.D. Radakovich, A.D. Silva, R. Todling and F. Verter, 2007. Skin temperature analysis and bias correction in a coupled land-atmosphere data assimilation system. J. Meteorol. Soc. Jpn., 85A, 205–228.Google Scholar
  10. Bouttier, F. and P. Courtier, 1999. Data assimilation concepts and methods. ECMWF training course notes.Google Scholar
  11. Bouttier, F., J.-F. Mahfouf and J. and Noilhan, 1993. Sequential assimilation of soil moisture from atmospheric low-level parameters. Part I: Sensitivity and calibration studies. J. Appl. Meteorol., 32, 1335–1351.Google Scholar
  12. Bouyssel, F., V. Cassé and J. Pailleux, 1999. Variational surface analysis from screen level atmospheric parameters. Tellus, 51A, 453–468.Google Scholar
  13. Bras, R. and I. Rodriguez-Iturbe,1985. Random Functions and Hydrology, Addison Wesley, Reading, MA, 590 pp.Google Scholar
  14. Bratseth, A.M., 1986. Statistical interpolation by means of successive corrections. Tellus, 38A, 439–447.Google Scholar
  15. Bruckler, L. and H. Witono, 1989. Use of remotely sensed soil moisture content as boundary conditions in soil-atmosphere water transport modeling: 2. Estimating soil water balance. Water Resour. Res., 25, 2437–2447.Google Scholar
  16. Callies, U., A. Rhodin and D. Eppel, 1998. A case study on variational soil moisture analysis from atmospheric observations. J. Hydrol., 212–213, 95–108.Google Scholar
  17. Calvet, J.-C., J. Noilhan and P. Bessemoulin, 1998. Retrieving the root-zone soil moisture from surface soil moisture or temperature estimates: A feasibility study based on field measurements. J. Appl. Meteorol., 37, 371–386.Google Scholar
  18. Caparrini, F., F. Castelli and D. Entekhabi, 2004. Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resour. Res., 40, W12515.1–W12515.15.Google Scholar
  19. Castelli, F., D. Entekhabi and E. Caporali, 1999. Estimation of surface heat flux and an index of soil moisture using adjoint-state surface energy balance. Water Resour. Res., 35, 3115–3125.Google Scholar
  20. Charney, J.G., M. Halem and R. Jastrow, 1969. Use of incomplete historical data to infer the present state of the atmosphere. J. Atmos. Sci., 26, 1160–1163.Google Scholar
  21. Chen, Y. and D. Zhang, 2006. Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv. Water Resour., 29, 1107–1122.Google Scholar
  22. Cosgrove, B.A. and P.R. Houser, 2002. The effect of errors in snow assimilation on land surface modeling. Preprints, 16th Conference on Hydrology, Orlando, FL, American Meteor Society, J136–J137.Google Scholar
  23. Cressman, G.P., 1959. An operational objective analysis system. Mon. Weather Rev., 87, 367–374.Google Scholar
  24. Crosson, W.L., C.A. Laymon, R. Inguva and M.P. Schamschula, 2002. Assimilating remote sensing data in a surface flux-soil moisture model. Hydro. Processes, 16, 1645–1662.Google Scholar
  25. Crow, W., 2003. Correcting land surface model predictions for the impact of temporally sparse rainfall rate measurements using an ensemble Kalman filter and surface brightness temperature observations. J. Hydrometeorol., 4, 960–973.Google Scholar
  26. Crow, W.T. and E. van Loon, 2006. Impact of incorrect model error assessment on the sequential assimilation of remotely sensed surface soil moisture. J. Hydrometeorol., 7, 421–432.Google Scholar
  27. 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.Google Scholar
  28. Daley, R., 1991. Atmospheric Data Analysis, Cambridge University Press, Cambridge, 460 pp.Google Scholar
  29. Dee, D.P. and A. da Silva, 1998. Data assimilation in the presence of forecast bias. Q. J. R. Meteorol. Soc., 124, 269–295.Google Scholar
  30. Dee, D.P. and R. Todling, 2000. Data assimilation in the presence of forecast bias: The GEOS moisture analysis. Mon. Weather Rev., 128, 3268–3282.Google Scholar
  31. De Lannoy, G.J.M., P.R. Houser, V.R.N. Pauwels and N.E.C. Verhoest, 2006. Assessment of model uncertainty for soil moisture through ensemble verification. J. Geophys. Res., 111, D10101.1–D10101.18.Google Scholar
  32. De Lannoy, G.J.M., P.R. Houser, V.R.N. Pauwels and N.E.C. Verhoest, 2007a. State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency. Water Resour. Res., 43, W06401, doi:10.1029/2006WR005100.Google Scholar
  33. De Lannoy, G.J.M., P.R. Houser, N.E.C. Verhoest and V.R.N. Pauwels, 2009. Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0. J. Hydrometeorol., 10, 766–779.Google Scholar
  34. De Lannoy, G.J.M., R.H. Reichle, P.R. Houser, K.R. Arsenault, V.R.N. Pauwels and N.E.C. Verhoest, 2010. Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. J. Hydrometeorol., 11, 352–369, doi:10.1175/2009JHM1194.1.Google Scholar
  35. De Lannoy, G.J.M., R.H. Reichle, P.R. Houser, V.R.N. Pauwels and N.E.C. Verhoest, 2007b. Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour. Res., 43, W09410, doi:10.1029/2006WR00544.Google Scholar
  36. Déry, S.J., V.V. Salomonson, M. Stieglitz, D.K. Hall and I. Appel, 2005. An approach to using snow areal depletion curves inferred from MODIS and its application to land surface modelling in Alaska. Hydrol. Processes, 19, 2755–2774.Google Scholar
  37. Dirmeyer, P., 2000. Using a global soil wetness dataset to improve seasonal climate simulation. J. Climate, 13, 2900–2921.Google Scholar
  38. Dong, J., J.P. Walker and P.R. Houser, 2005. Factors affecting remotely sensed snow water equivalent uncertainty. Remote Sens. Environ., 97, 68–82, doi:10.1016/j.rse.2005.04.010.Google Scholar
  39. Dong, J., J.P. Walker, P.R. Houser and C. Sun, 2007. Scanning multichannel microwave radiometer snow water equivalent assimilation. J. Geophys. Res., 112, D07108, doi:10.1029/2006JD007209.Google Scholar
  40. Douville, H., P. Viterbo, J.-F. Mahfouf and A.C.M. Beljaars, 2000. Evaluation of the optimum interpolation and nudging techniques for soil moisture analysis using fife data. Mon. Weather Rev., 128, 1733–1756.Google Scholar
  41. Duan, Q., S. Sorooshian and V.K. Gupta, 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 28, 1015–1031.Google Scholar
  42. Dunne, S. and D. Entekhabi, 2005. An ensemble-based reanalysis approach to land data assimilation. Water Resour. Res., 41, W02013.1–W02013.18.Google Scholar
  43. Dunne, S. and D. Entekhabi, 2006. Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment. Water Resour. Res., 42, W01407.1–W01407.15.Google Scholar
  44. Durand, M. and S.A. Margulis, 2006. Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent. J. Hydrometeorol., 7, 443–457.Google Scholar
  45. Durand, M. and S.A. Margulis, 2007. Correcting first-order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme. J. Geophys. Res., 112, D13121.1–D13121.15.Google Scholar
  46. Durand, M. and S.A. Margulis, 2008. Effects of uncertainty magnitude and accuracy on assimilation of multiscale measurements for snowpack characterization. J. Geophys. Res., 113, D02105.1–D02105.17.Google Scholar
  47. Eigbe, U., M. Beck, H. Weather and F. Hirano, 1998. Kalman filtering in groundwater flow modelling: Problems and prospects. Stochast. Hydrol. Hydraul., 12, 15–32.Google Scholar
  48. Entekhabi, D., H. Nakamura and E.G. Njoku, 1994. Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE Trans. Geosci. Rem. Sens., 32, 438–448.Google Scholar
  49. Evensen, G., 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143–10162.Google Scholar
  50. Evensen, G., 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–367.Google Scholar
  51. Francois, C., A. Quesney and C. Ottlé, 2003. Sequential assimilation of ERS-1 SAR data into a coupled land surface-hydrological model using an extended Kalman filter. J. Hydrometeorol., 4, 473–487.Google Scholar
  52. Friedland, B., 1969. Treatment of bias in recursive filtering. IEEE Trans. Autom. Control, AC-14, 359–367.Google Scholar
  53. Galantowicz, J.F., D. Entekhabi and E.G. Njoku, 1999. Tests of sequential data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness. IEEE Trans. Geosci. Rem. Sens., 37, 1860–1870.Google Scholar
  54. Georgakakos, K.P. and O.W. Baumer, 1996. Measurement and utilization of on-site soil moisture data. J. Hydrol., 184, 131–152.Google Scholar
  55. Gove, J.H. and D.Y. Hollinger, 2006. Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface atmosphere exchange. J. Geophys. Res., 111, D08S07.1–D08S07.21.Google Scholar
  56. Heathman, G., P. Starks, L. Ahuj and T. Jackson, 2003. Assimilation of soil moisture to estimate profile soil water content. J. Hydrol., 279, 1–17.Google Scholar
  57. Hebson, C. and E. Wood, 1985. Partitioned state and parameter estimation for real-time flood forecasting. Appl. Math. Comput., 17, 357–374.Google Scholar
  58. Hess, R., 2001. Assimilation of screen-level observations by variational soil moisture analysis. Meteorol. Atmos. Phys., 77, 145–154.Google Scholar
  59. Hoeben, R. and P.A. Troch, 2000. Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour. Res., 36, 2805–2819.Google Scholar
  60. Hollingsworth, A. and P. Lönnberg, 1989. The verification of objective analyses: Diagnostics of analysis system performance. Meteorol. Atmos. Phys., 40, 3–27.Google Scholar
  61. Houser, P., M.F. Hutchinson, P. Viterbo, J. Hervé Douville and S.W. Running, 2004. Terrestrial data assimilation, Chapter C.4. In Vegetation, Water, Humans and the Climate, Global Change – The IGB Series, Kabat, P. et al. (eds.), Springer, Berlin, pp 273–287.Google Scholar
  62. Houser, P.R., W.J. Shuttleworth, J.S. Famiglietti, H.V. Gupta, K.H. Syed and D.C. Goodrich, 1998. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour. Res., 34, 3405–3420.Google Scholar
  63. Houtekamer, P.L. and H.L. Mitchell, 1998. Data assimilation using a Ensemble Kalman filter techniques. Mon. Weather Rev., 126, 796–811.Google Scholar
  64. Hu, Y., X. Gao, W. Shuttleworth, H. Gupta and P. Viterbo, 1999. Soil moisture nudging experiments with a single column version of the ECMWF model. Q. J. R. Meteorol. Soc., 125, 1879–1902.Google Scholar
  65. Hurkmans, R., C. Paniconi and P.A. Troch, 2006. Numerical assessment of a dynamical relaxation data assimilation scheme for a catchment hydrological model. Hydrol. Processes, 20, 549–563.Google Scholar
  66. Jackson, T.J., T.J. Schmugge, A.D. Nicks, G.A. Coleman and E.T. Engman, 1981. Soil moisture updating and microwave remote sensing for hydrological simulation. Hydrol. Sci. Bull., 26, 305–319.Google Scholar
  67. Jazwinski, A.H., 1970. Stochastic Processes and Filtering Theory, Vol. 64. Academic Press, New York, 376 pp.Google Scholar
  68. Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Trans. ASME, Ser. D, J. Basic Eng., 82, 35–45.Google Scholar
  69. Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteorol. Soc., 77, 437–471.Google Scholar
  70. Katul, G.G., O. Wendroth, M.B. Parlange, C.E. Puente, M.V. Folegatti and D.R. Nielsen, 1993. Estimation of in situ hydraulic conductivity function from nonlinear filtering theory. Water Resour. Res., 29, 1063–1070.Google Scholar
  71. 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.Google Scholar
  72. Koster, R.D., M. Suarez, P. Liu, U. Jambor, A. Berg, M. Kistler, R. Reichle, M. Rodell and J. Famiglietti, 2004. Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeorol., 5, 1049–1063.Google Scholar
  73. Kostov, K.G. and T.J. Jackson, 1993. Estimating profile soil moisture from surface layer measurements – A review. In: Proceedings of the International Society for Optical Engineering,Vol. 1941. Orlando, FL, pp 125–136.Google Scholar
  74. Kumar, P. and A.L. Kaleita, 2003. Assimilation of near-surface temperature using extended Kalman filter. Adv. Water Resour., 26, 79–93.Google Scholar
  75. Lakshmi, V., 2000. A simple surface temperature assimilation scheme for use in land surface models. Water Resour. Res., 36, 3687–3700.Google Scholar
  76. Li, J. and S. Islam, 1999. On the estimation of soil moisture profile and surface fluxes partitioning from sequential assimilation of surface layer soil moisture. J. Hydrol., 220, 86–103.Google Scholar
  77. Li, J. and S. Islam, 2002. Estimation of root zone soil moisture and surface fluxes partitioning using near surface soil moisture measurements. J. Hydrol., 259, 1–14.Google Scholar
  78. Lorenc, A., 1981. A global three-dimensional multivariate statistical interpolation scheme. Mon. Weather Rev., 109, 701–721.Google Scholar
  79. Lorenc, A.C., R.S. Bell and B. Macpherson, 1991. The meteorological office analysis correction data assimilation scheme. Q. J. R. Meteorol. Soc., 117, 59–89.Google Scholar
  80. Mahfouf, J.-F., 1991. Analysis of soil moisture from near-surface parameters: A feasibility study. J. Appl. Meteorol., 30, 1534–1547.Google Scholar
  81. Mahfouf, J. and P. Viterbo, 2001. Land surface assimilation. Meteorological Training Course Lecture Series ECMWF.Google Scholar
  82. Margulis, S.A., D. McLaughlin, D. Entekhabi and S. Dunne, 2002. Land data assimilation of soil moisture using measurements from the Southern Great Plains 1997 field experiment. Water Resour. Res., 38, 35.1–35.18.Google Scholar
  83. Margulis, S.A., E.F. Wood and P.A. Troch, 2006. A terrestrial water cycle: Modeling and data assimilation across catchment scales. J. Hydrometeorol., 7, 309–311.Google Scholar
  84. Maybeck, P.S., 1979. Stochastic Models, Estimation, and Control, Vol. 1 (Vol. 141). Academic Press, Toronto, 423 pp.Google Scholar
  85. McLaughlin, D., 1995. Recent developments in hydrologic data assimilation. In U.S. National Report to the IUGG (1991–1994). Rev. Geophys., 33(supplement), 977–984.Google Scholar
  86. McLaughlin, D., 2002. An integrated approach to hydrologic data assimilation: Interpolation, smoothing, and filtering. Adv. Water Resour., 25, 1275–1286.Google Scholar
  87. Milly, P.C.D., 1986. Integrated remote sensing modelling of soil moisture: Sampling frequency, response time, and accuracy of estimates. Integrated Design of Hydrological Networks – Proceedings of the Budapest Symposium, IAHS Publication No. 158, 201–211.Google Scholar
  88. Milly, P. and Z. Kabala, 1986. Integrated modelling and remote sensing of soil moisture. In Hydrologic applications of space technology – Proceedings of the Cocoa Beach Workshop, Vol. 158. Florida, pp 201–211.Google Scholar
  89. Montaldo, N. and J.D. Albertson, 2003. Multi-scale assimilation of surface soil moisture for robust root zone moisture predictions. Adv. Water Resour., 26, 33–44.Google Scholar
  90. Montaldo, N., J.D. Albertson, M. Mancini and G. Kiely, 2001. Robust simulation of root zone soil moisture with assimilation of suface soil moisture data. Water Resour. Res., 37, 2889–2900.Google Scholar
  91. Moradkhani, H., S. Sorooshian, H.V. Gupta and P.R. Houser, 2005. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour., 28, 135–147.Google Scholar
  92. Nichols, N.K., 2001. State estimation using measured data in dynamic system models, Lecture notes for the Oxford/RAL Spring School in Quantitative Earth Observation.Google Scholar
  93. Ottlé, C. and D. Vidal-Madjar, 1994. Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY Region. J. Hydrol., 158, 241–264.Google Scholar
  94. Oudin, L., A. Weisse, C. Loumage and S. Le Hégarat-Mascle, 2003. Assimilation of soil moisture into hydrological models for flood forecasting: A variational approach. Can. J. Rem. Sens., 29, 679–686.Google Scholar
  95. Pan, M. and E.F. Wood, 2006. Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J. Hydrometeorol., 7, 534–547.Google Scholar
  96. Paniconi, C., M. Marrocu, M. Putti and M. Verbunt, 2003. Newtonian nudging for a Richards equation-based distributed hydrological model. Adv. Water Resour., 26, 161–178.Google Scholar
  97. Parrish, D. and J. Derber, 1992. The national meteorological center’s spectral statistical interpolation analysis system. Mon. Weather Rev., 120, 1747–1763.Google Scholar
  98. Pathmathevan, M., T. Koike, X. Lin and H. Fujii, 2003. A simplified land data assimilation scheme and its application to soil moisture experiments in 2002 (SMEX02). Water Resour. Res., 39, SWC6.1–SWC6.20.Google Scholar
  99. Pauwels, V.R.N. and G.J.M. De Lannoy, 2006. Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge. J. Hydrometeorol., 7, 458–477.Google Scholar
  100. Pauwels, V.R.N., R. Hoeben, N.E.C. Verhoest and F.P. De Troch, 2001. The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation. J. Hydrol., 251, 88–102.Google Scholar
  101. Pauwels, V.R.N., N.E.C. Verhoest, G.J.M. De Lannoy, V. Guissard, C. Lucau and P. Defourny, 2007. Optimization of a coupled hydrology/crop growth model through the assimilation of observed soil moisture and LAI values using an Ensemble Kalman Filter. Water Resour. Res., 43, W04421, doi:10.1029/2006WR004942.Google Scholar
  102. Pleim, J.E. and A. Xiu, 2003. Development of a land surface model. Part II: Data assimilation. J. Appl. Meteorol., 42, 1811–1822.Google Scholar
  103. Porter, D., B. Gibbs, W. Jones, P. Huyakorn, L. Hamm and G. Flach, 2000. Data fusion modeling for groundwater systems. J. Contam. Hydrol., 42, 303–335.Google Scholar
  104. Prevot, L., R. Bernard, O. Taconet, et al., 1984. Evaporation from a bare soil evaluated using a soil water transfer model and remotely sensed surface soil moisture data. Water Resour. Res., 20, 311–316.Google Scholar
  105. Radakovich, J.D., P.R. Houser, A. da Silva and M.G. Bosilovich, 2001. Results from global land-surface data assimilation methods. Proceedings AMS 5th Symposium on Integrated Observing Systems, Albuquerque, NM, 14–19 January, pp 132–134.Google Scholar
  106. Reichle, R.H., W.T. Crow and C.L. Keppenne, 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation, Water Resour. Res., 44, W03423, doi:10.1029/2007WR006357.Google Scholar
  107. Reichle, R.H., D. Entekhabi and D.B. McLaughlin, 2001. Downscaling of radiobrightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach. Water Resour. Res., 37, 2353–2364.Google Scholar
  108. Reichle, R.H. and R. Koster, 2003. Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J. Hydrometeorol., 4, 1229–1242.Google Scholar
  109. Reichle, R.H. and R. Koster, 2004. Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501.1–L19501.4.Google Scholar
  110. Reichle, R.H. and D.B. McLaughlin, 2001. Variational data assimilation of microwave radiobrightness observations for land surface hydrologic applications. IEEE Trans. Geosci. Rem. Sens., 39, 1708–1718.Google Scholar
  111. Reichle, R.H., D.B. McLaughlin and D. Entekhabi, 2002a. Hydrologic data assimilation with the ensemble Kalman filter. Mon. Weather Rev., 120, 103–114.Google Scholar
  112. Reichle, R.H., J.P. Walker, P.R. Houser and R.D. Koster, 2002b. Extended versus ensemble Kalman filtering for land data assimilation. J. Hydrometeorol., 3, 728–740.Google Scholar
  113. Rhodin, A., F. Kucharski, U. Callies, D. Eppel and W. Wergen, 1999. Variational analysis of effective soil moisture from screen-level atmospheric parameters: Application to a short-range weather forecast model. Q. J. R. Meteorol. Soc., 125, 2427–2448.Google Scholar
  114. Rodell, M. and P.R. Houser, 2004. Updating a land surface model with MODIS-derived snow cover. J. Hydrometeorol., 5, 1064–1075.Google Scholar
  115. Rood, R.B., S.E. Cohn and L. Coy, 1994. Data assimilation for EOS: The value of assimilated data, Part 1. Earth Observer, 6, 23–25.Google Scholar
  116. Rüdiger, C., G. Hancock, H.M. Hemakumara, B. Jacobs, J.D. Kalma, C. Martinez, M. Thyer, J.P. Walker, T. Wells and G.R. Willgoose, 2007. The Goulburn River experimental catchment data set. Water Resour. Res., 43, W10403, doi:10.1029/2006WR005837.Google Scholar
  117. Rüdiger, C., J.P. Walker, J.D. Kalma, G.R. Willgoose and P.R. Houser, 2005. Root zone soil moisture retrieval using streamflow and surface soil moisture data assimilation. In MODSIM 2005 International Congress on Modelling and Simulation, Zerger, A. and Argent, R.M. (eds.), Modelling and Simulation Society of Australia and New Zealand, Inc., Melbourne, Australia, 12–15 December, 2005, pp 1458–1464.Google Scholar
  118. Schuurmans, J., P. Troch, A. Veldhuizen, W. Bastiaansen and M. Bierkens, 2003. Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv. Water Resour., 26, 151–159.Google Scholar
  119. Seuffert, G., H. Wilker, P. Viterbo, M. Drusch and J.-F. Mahfouf, 2004. The usage of screen-level parameters and microwave brightness temperature for soil moisture analysis. J. Hydrometeorol., 5, 516–531.Google Scholar
  120. Slater, A.G. and M. Clark, 2006. Snow data assimilation via an ensemble Kalman filter. J. Hydrometeorol., 7, 478–493.Google Scholar
  121. Stauffer, D.R. and N.L. Seaman, 1990. Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Weather Rev., 118, 1250–1277.Google Scholar
  122. Stieglitz, M., D. Rind, J. Famiglietti and C. Rosenzweig, 1997. An efficient approach to modeling the topographic control of surface hydrology for regional and global climate modeling. J. Climate, 10, 118–137.Google Scholar
  123. Sun, C., J.P. Walker and P.R. Houser, 2004. A methodology for snow data assimilation in a land surface model. J. Geophys. Res., 109, D08108.1–D08108.12.Google Scholar
  124. Thiemann, M., M. Trosset, H. Gupta and S. Sorooshian, 2001. Bayesian recursive parameter estimation for hydrological models. Water Resour. Res., 37, 2521–2535.Google Scholar
  125. Turner, M.R.J., J.P. Walker and P.R. Oke, 2007. ensemble member generation for sequential data assimilation. Remote Sens. Environ., 112, doi:10.1016/j.rse.2007.02.042.Google Scholar
  126. van Loon, E.E. and P.A. Troch, 2001. Directives for 4-D soil moisture data assimilation in hydrological modelling. IAHS, 270, 257–267.Google Scholar
  127. Viterbo, P. and A. Beljaars, 1995. An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate, 8, 2716–2748.Google Scholar
  128. Vrugt, J.A., H.V. Gupta, B. O’Nualláin and W. Bouten, 2006. Real-time data assimilation for operational ensemble streamflow forecasting. J. Hydrometeorol., 7, 548–565.Google Scholar
  129. Walker J.P. and P.R. Houser, 2001. A methodology for initialising soil moisture in a global climate model: Assimilation of near-surface soil moisture observations. J. Geophys. Res., 106, 11761–11774.Google Scholar
  130. Walker, J.P. and P.R. Houser, 2004. Requirements of a global near-surface soil moisture satellite mission: Accuracy, repeat time, and spatial resolution. Adv. Water Resour., 27, 785–801.Google Scholar
  131. Walker, J.P. and P.R. Houser, 2005. Hydrologic data assimilation. In Advances in Water Science Methodologies, Aswathanarayana, A. (ed.), A.A. Balkema, The Netherlands, 230 pp.Google Scholar
  132. Walker, J.P., P.R. Houser and R. Reichle, 2003. New technologies require advances in hydrologic data assimilation. EOS, 84, 545–551.Google Scholar
  133. Walker, J.P., G.R. Willgoose and J.D. Kalma, 2001a. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithms. Adv. Water Resour., 24, 631–650.Google Scholar
  134. Walker, J.P., G.R. Willgoose and J.D. Kalma, 2001b. One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: A simplified soil moisture model and field application. J. Hydrometeorol., 2, 356–373.Google Scholar
  135. Walker, J.P., G.R. Willgoose and J.D. Kalma, 2002. Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application. Water Resour. Res., 38, 1301, doi:10.1029/2002WR001545.Google Scholar
  136. Wendroth, O., H. Rogasik, S. Koszinski, C.J. Ritsema, L.W. Dekker and D.R. Nielsen, 1999. State-space prediction of field-scale soil water content time series in a sandy loam. Soil & Till. Res., 50, 85–93.Google Scholar
  137. Wilker, H., M. Drusch, G. Seuffert and C. Simmer, 2006. Effects of the near-surface soil moisture profile on the assimilation of L-band microwave brightness temperature. J. Hydrometeorol., 7, 433–442.Google Scholar
  138. Wingeron, J.-P., A. Olioso, J.-C. Calvet and P. Bertuzzi, 1999. Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling. Water Resour. Res., 35, 3735–3745.Google Scholar
  139. WMO, 1992. Simulated real-time intercomparison of hydrological models (Tech. Rep. No. 38). Geneva.Google Scholar
  140. Zaitchik, B.F., M. Rodell and R. Reichle, 2008. Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi river basin. J. Hydrometeorol., 9, 535–548.Google Scholar
  141. Zhang, H. and C.S. Frederiksen, 2003. Local and nonlocal impacts of soil moisture initialization on AGCM seasonal forecasts: A model sensitivity study. J. Climate, 16, 2117–2137.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Paul R. Houser
    • 1
    Email author
  • Gabriëlle J.M. De Lannoy
    • 1
    • 2
  • Jeffrey P. Walker
    • 3
  1. 1.George Mason UniversityFairfaxUSA
  2. 2.Ghent UniversityGhentBelgium
  3. 3.Department of Civil and Environmental EngineeringThe University of MelbourneVictoriaAustralia

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