Advertisement

Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems

  • William A. Lahoz
  • Gabriëlle J. M. De Lannoy
Chapter
Part of the Space Sciences Series of ISSI book series (SSSI, volume 46)

Abstract

This paper reviews the conceptual problems limiting our current knowledge of the hydrological cycle over land. We start from the premise that to understand the hydrological cycle we need to make observations and develop dynamic models that encapsulate our understanding. Yet, neither the observations nor the models could give a complete picture of the hydrological cycle. Data assimilation combines observational and model information and adds value to both the model and the observations, yielding increasingly consistent and complete estimates of hydrological components. In this review paper we provide a historical perspective of conceptual problems and discuss state-of-the-art hydrological observing, modelling and data assimilation systems.

Keywords

Hydrological cycle Earth observation Land surface models Data assimilation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams DKRM, Fernandes S, Kursinski ER, Maia JM, Sapucci LF, Machado LAT, Vitorello I, Galera Monico JF, Holub KL, Gutman S, Filizola N, Bennett RA (2011) A dense GNSS meteorological network for observing deep convection in the Amazon. Atmos Sci Lett 12. doi: 10.1002/asl.312 Google Scholar
  2. Al Bitar A, Leroux D, Kerr YH, Merlin O, Richaume P, Sahoo A, Wood EF (2012) Evaluation of SMOS soil moisture products over continental U.S. using the SCAN/SNOTEL network. IEEE Trans Geosci Remote Sens 50:1572–1586Google Scholar
  3. Albergel C, Rüdiger C, Carrer D, Calvet J-C, Fritz N, Naeimi V, Bartalis Z, Hasenauer S (2009) An evaluation of ASCAT surface soil moisture products with in situ observations in Southwestern France. Hydrol Earth Syst Sci 13:115–124Google Scholar
  4. Albergel C, Calvet J-C, Mahfouf J-F, Rüdiger C, Barbu AL, Lafont S, Roujean J-L, Walker JP, Crapeau M, Wigneron J-P (2010) Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France. Hydrol Earth Syst Sci 14:1109–1124Google Scholar
  5. Andreadis KM, Lettenmaier DP (2006) Assimilating remotely sensed snow observation into a macroscale hydrology model. Adv Water Resour 29:872–886Google Scholar
  6. Andreadis K, Clark E, Lettenmaier D, Alsdorf D (2007) Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophys Res Lett 34:L10403Google Scholar
  7. Andreadis KM, Liang D, Tsang L, Lettenmaier DP, Josberger EG (2008) Characterization of errors in a coupled snow hydrology microwave emission model. J Hydrometeorol 9:149–164Google Scholar
  8. Arsenault K, Houser P, Dirmeyer P, De Lannoy G (2013) Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets. J Geophys Res (in press)Google Scholar
  9. Aubert D, Loumagne C, Oudin L (2003) Sequential assimilation of soil moisture and streamflow data into a conceptual rainfall-runoff model. J Hydrol 280:145–161Google Scholar
  10. Balsamo G, Bouyssel F, Noilhan J (2004) A simplified bi-dimensional variational analysis of soil moisture from screen-level observations in a mesoscale numerical weather-prediction model. Q J R Meteorol Soc 130:895–915Google Scholar
  11. Balsamo G, Bélair S, Deblonde G (2006) A global root-zone soil moisture analysis using simulated L-band brightness temperature in preparation for the hydros satellite mission. J Hydrometeorol 7:1126–1146Google Scholar
  12. Balsamo G, Viterbo P, Beljaars A, van den Hurk B, Hirschi M, Betts AK, Scipal K (2009) A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the integrated forecast system. J Hydrometeorol 10. doi: 10.1175/2008JHM1068.1 Google Scholar
  13. Bannister RN (2008a) A review of forecast error covariance statistics in atmospheric variational data assimilation. I: characteristics and measurements of forecast error covariances. Q J R Meteorol Soc 134:1951–1970Google Scholar
  14. Bannister RN (2008b) A review of forecast error covariance statistics in atmospheric variational data assimilation. II: modelling the forecast error covariances. Q J R Meteorol Soc 134:1971–1996Google Scholar
  15. Barrett D, Renzullo LJ (2009) On the efficacy of combining thermal and microwave satellite data as observational constraints for root-zone soil moisture estimation. J Hydrometeorol 10:1109–1127Google Scholar
  16. Bartalis Z, Wagner W, Naeimi V, Hasenauer S, Scipal K, Bonekamp H, Figa J, Anderson C (2007) Initial soil moisture retrievals from the METOP-A advanced Scatterometer (ASCAT). Geophys Res Lett 34:L20401. doi: 10.1029/2007GL031088 CrossRefGoogle Scholar
  17. Bateni SM, Huang C, Margulis SA, Podest E, McDonald K (2013) Feasibility of characterizing snowpack and the freeze-thaw state of underlying soil using multifrequency active/passive microwave data. IEEE Trans Geosci Remote Sens. doi: 10.1109/TGRS.2012.2229466 Google Scholar
  18. Beljaars A, Viterbo P, Miller M, Betts A (1996) The anomalous rainfall over the United States during July 1993: sensitivity to land surface parameterization and soil anomalies. Mon Weather Rev 124:362–383Google Scholar
  19. Best MJ, Pryor M, Clark DB, Rooney GG, Essery RLH, Ménard CB, Edwards JM, Hendry MA, Porson A, Gedney N, Mercado LM, Sitch S, Blyth E, Boucher O, Cox PM, Grimmond CSB, Harding RJ (2011) The Joint UK Land Environment Simulator (JULES), model description—part 1: energy and water fluxes. Geosci Model Dev 4:677–699Google Scholar
  20. Beven K (1989) Changing ideas in hydrology: the case of physically-based models. J Hydrol 105:157–172Google Scholar
  21. Beven BJ, Kirkby MJ (1979) A physically-based variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69Google Scholar
  22. Biancamaria S, Durand M, Andreadis KM, Bates PD, Boone A, Mognard NM, Rodríguez E, Alsdorf DE, Lettenmaier DP, Clark EA (2010) Assimilation of virtual wide swath altimetry to improve Arctic river modelling. Remote Sens Environ. doi: 10.1016/j.rse.2010.09.008 CrossRefGoogle Scholar
  23. Bircher S, Balling JE, Skou N, Kerr YH (2012) Validation of SMOS brightness temperatures during the HOBE airborne campaign, Western Denmark. IEEE Trans Geosci Remote Sens 50:1468–1482Google Scholar
  24. Boni G, Entekhabi D, Castelli F (2001) Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resour Res 37:1713–1722Google Scholar
  25. Boone A (1999) Modelisation des processus hydrologiques dans le schema de surface ISBA: Inclusion d’un reservoir hydrologique, du gel et modelisation de la neige, PhD thesis, University Paul Sabatier, Toulouse, France, 2000, 252 ppGoogle Scholar
  26. Bosilovich MG, Radakovich JD, Silva AD, Todling R, Verter F (2007) Skin temperature analysis and bias correction in a coupled land-atmosphere data assimilation system. J Meteorol Soc Jpn 85A:205–228Google Scholar
  27. Boulet G, Kerr Y, Chehbouni A (2002) Deriving catchment-scale water and energy balance parameters using data assimilation based on extended Kalman filtering. Hydrol Sci J des Sci Hydrol 47:449–467Google Scholar
  28. Bouttier F, Courtier P (1999) Data assimilation concepts and methods. ECMWF training notes, March 1999, available from http://www.ecmwf.int
  29. Branstetter ML, Erickson DJ III (2003) Continental runoff dynamics in the Community Climate System Model 2 (CCSM2) control simulation. J Geophys Res 108:4550. doi: 10.1029/2002JD003212 CrossRefGoogle Scholar
  30. Brasnett B (1999) A global analysis of snow depth for numerical weather prediction. J Appl Meteorol 38:726–740Google Scholar
  31. Calvet J-C, Noilhan J, Bessemoulin P (1998) Retrieving the root-zone soil moisture from surface soil moisture or temperature estimates: a feasibility study based on field measurements. J Appl Meteorol 37:371–386Google Scholar
  32. Calvet J-C, Fritz N, Froissard F, Suquia D, Petitpa A, Piguet B (2007) In-situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network. International geoscience and remote sensing symposium, IGARSS, 23–28 July 2007, Barcelona, Spain, pp 1196–1199. doi: 10.1109/IGARSS.2007.4423019
  33. Camporese M, Paniconi C, Putti M, Salandin P (2009) Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resour Res 45:W10421Google Scholar
  34. Caparrini F, Castelli F, Entekhabi D (2004) Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resour Res 40:W12515.1–W12515.15Google Scholar
  35. Castelli F, Entekhabi D, Caporali E (1999) Estimation of surface heat flux and an index of soil moisture using adjoint-state surface energy balance. Water Resour Res 35:3115–3125Google Scholar
  36. Castro R, Gutierrez A, Barbosa J (2012) A first set of techniques to detect radio frequency interferences and mitigate their impact on SMOS data. IEEE Trans Geosci Remote Sens 50:1440–1447Google Scholar
  37. Clark M, Vrugt J (2006) Unraveling uncertainties in hydrologic model calibration: addressing the problem of compensatory parameters. Geophys Res Lett 33:L06406.1–L06406.5Google Scholar
  38. Clark MP, Slater AG, Barrett AP, Hay LE, McCabe GJ, Rajagopalan B, Leavesley GH (2006) Assimilation of snow covered area information into hydrologic and land-surface models. Adv Water Resour 29:1209–1221Google Scholar
  39. Clark DB, Mercado LM, Sitch S, Jones CD, Gedney N, Best MJ, Pryor M, Rooney GG, Essery RLH, Blyth E, Boucher O, Harding RJ, Huntingford C, Cox PM (2011a) The Joint UK Land Environment Simulator (JULES), model description—part 2: carbon fluxes and vegetation dynamics. Geosci Model Dev 4:701–722Google Scholar
  40. Clark MP, Hendrikx L, Slater AG, Kavetski D, Anderson B, Cullen NJ, Kerr T, Hreinsson EÖ, Woods RA (2011b) Representing spatial variability of snow water equivalent in hydrologic and land-surface models: a review. Water Resour Res 47:W07539Google Scholar
  41. Crosson WL, Laymon CA, Inguva R, Schamschula MP (2002) Assimilating remote sensing data in a surface flux–soil moisture model. Hydrol Process 16:1645–1662Google Scholar
  42. 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 4:960–973Google Scholar
  43. Crow WT (2007) A novel method for quantifying value in spaceborne soil moisture retrievals. J Hydrometeorol 8:56–67. doi: 10.1175/JHM553.1 CrossRefGoogle Scholar
  44. Crow WT, Bolten JD (2007) Estimating precipitation errors using spaceborne surface soil moisture retrievals. Geophys Res Lett 34:L08403Google Scholar
  45. Crow WT, Reichle RH (2008) Comparison of adaptive filtering techniques for land surface data assimilation. Water Resour Res 44:W08423. doi: 10.1029/2008WR006883 CrossRefGoogle Scholar
  46. Crow WT, Ryu D (2009) A new data assimilation approach for improving runoff prediction using remotely sensed soil moisture retrievals. Hydrol Earth Syst Sci 13:1–16Google Scholar
  47. Crow WT, van den Berg J (2010) An improved approach for estimating observation and model error parameters in soil moisture data assimilation. Water Resour Res 46:W12519Google Scholar
  48. Crow WT, van Loon E (2006) Impact of incorrect model error assessment on the sequential assimilation of remotely sensed surface soil moisture. J Hydrometeorol 7:421–432Google Scholar
  49. Crow WT, Wood EF (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 26:137–149Google Scholar
  50. Crow WT, Zhan X (2007) Continental-scale evaluation of remotely sensed soil moisture products. IEEE Geosci Remote Sens Lett 4:451–455. doi: 10.1109/LGRS.2007.896533 CrossRefGoogle Scholar
  51. Crow WT, Miralles DG, Cosh MH (2010) A quasi-global evaluation system for satellite-based surface soil moisture retrievals. IEEE Geosci Remote Sens Lett 48:2516–2527Google Scholar
  52. Crow WT, Berg AA, Cosh MH, Loew A, Mohanty BP, Panciera R, de Rosnay P, Ryu D, Walker JP (2012) Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev Geophys Res 50:RG2002. doi: 10.1029/2011RG000372 CrossRefGoogle Scholar
  53. dall’Amico JT, Schlenz F, Loew A, Mauser W (2012) First results of SMOS soil moisture validation in the Upper Danube catchment. IEEE Trans Geosci Remote Sens 50:1507–1516Google Scholar
  54. Davenport I, Sandells M, Gurney R (2012) The effects of variation in snow properties on passive microwave snow mass estimation. Remote Sens Environ 118:168–175Google Scholar
  55. De Lannoy GJM, Houser PR, Pauwels VRN, Verhoest NEC (2006) Assessment of model uncertainty for soil moisture through ensemble verification. J Geophys Res 111:D10101.1–D10101.18. doi: 10.1029/2005JD006367 CrossRefGoogle Scholar
  56. De Lannoy GJM, Houser PR, Pauwels VRN, Verhoest NEC (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 CrossRefGoogle Scholar
  57. De Lannoy GJM, Reichle RH, Houser PR, Pauwels VRN, Verhoest NEC (2007b) Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour Res 43:W09410. doi: 10.1029/2006WR00544 CrossRefGoogle Scholar
  58. De Lannoy GJM, Houser PR, Verhoest NEC, Pauwels VRN (2009) Adaptive soil moisture profile filtering for horizontal information propagation in the independent column-based CLM2.0. J Hydrometeorol 10:766–779Google Scholar
  59. De Lannoy GJM, Reichle RH, Houser PR, Arsenault KR, Pauwels VRN, Verhoest NEC (2010) Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. J Hydrometeorol 11:352–369. doi: 10.1175/2009JHM1194.1 CrossRefGoogle Scholar
  60. De Lannoy GJM, Reichle R, Arsenault K, Houser P, Kumar S, Verhoest N, Pauwels V (2012) Multiscale assimilation of advanced microwave scanning radiometer-EOS snow water equivalent and moderate resolution imaging spectroradiometer snow cover fraction observations in northern Colorado. Water Resour Res 48:W01522Google Scholar
  61. De Lannoy GJM, Reichle RH, Pauwels VRN (2013) Global calibration of the GEOS-5 L-band microwave radiative transfer model over land using SMOS observations. J Hydrometeorol (in press)Google Scholar
  62. de Rosnay P, Calvet J-C, Kerr Y, Wigneron J-P, Lemaître F et al (2006) SMOSREX: a long term field campaign experiment for soil moisture and land surface processes remote sensing. Remote Sens Environ 102(377–389):2006Google Scholar
  63. de Rosnay P, Balsamo G, Albergel C, Muñoz-Sabater J, Isaksen L (2012a) Initialisation of land surface variables for numerical weather prediction. Surv Geophys. doi: 10.1007/s10712-012-9207-x Google Scholar
  64. de Rosnay PD, Drusch M, Vasiljevic D, Balsamo G, Albergel C, Isaksen L (2012b) A simplified extended Kalman filter for the global operational soil moisture analysis at ECMWF. Q J R Meteorol Soc. doi: 10.1002/qj.2023 Google Scholar
  65. Deardorff JW (1977) A parameterization of ground surface moisture content for use in atmospheric prediction models. J Appl Meteorol 16:1182–1185Google Scholar
  66. DeChant C, Moradkhani H (2010) Radiance data assimilation for operational snow and streamflow forecasting. Adv Water Resour 34:351–364Google Scholar
  67. Decharme B, Douville H (2006) Introduction of a sub-grid hydrology in the ISBA land surface model. Clim Dyn 26:65–78Google Scholar
  68. Decharme B, Douville H (2007) Global validation of the ISBA sub-grid hydrology. Clim Dyn 29:21–37Google Scholar
  69. Decharme B, Douville H, Boone A, Habets F, Noilhan J (2006) Impact of an exponential profile of saturated hydraulic conductivity within the ISBA LSM: simulations over the Rhône basin. J Hydrometeorol 7:61–80Google Scholar
  70. Dee DP (2005) Bias and data assimilation. Q J R Meteorol Soc 131:3323–3343Google Scholar
  71. Déry SJ, Salomonson VV, Stieglitz M, Hall DK, Appel I (2005) An approach to using snow areal depletion curves inferred from MODIS and its application to land surface modelling in Alaska. Hydrol Process 19:2755–2774Google Scholar
  72. Desroziers G, Berre L, Chapnik B, Poli P (2005) A simple method to diagnose and adapt observation and background errors. Q J R Meteorol Soc 131:3385–3396Google Scholar
  73. Dharssi I, Bovis KJ, Macpherson B, Jones CP (2011) Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrol Earth Syst Sci 15:2729–2746Google Scholar
  74. Dirmeyer P (2000) Using a global soil wetness dataset to improve seasonal climate simulation. J Clim 13:2900–2921Google Scholar
  75. Dong J, Walker JP, Houser PR, Sun C (2007) Scanning multichannel microwave radiometer snow water equivalent assimilation. J Geophys Res 112:D07108. doi: 10.1029/2006JD007209 CrossRefGoogle Scholar
  76. Dorigo W, Scipal K, Parinussa RM, Liu YY, Wagner W, de Jeu RAM, Naeimi V (2010) Error characterization of global active and passive microwave soil moisture data sets. Hydrol Earth Syst Sci 14:2605–2616Google Scholar
  77. Douville H, Viterbo P, Mahfouf J-F, Beljaars ACM (2000) Evaluation of optimal interpolation and nudging techniques for soil moisture analysis using FIFE data. Mon Weather Rev 128:1733–1756Google Scholar
  78. Draper CS, Mahfouf J-F, Walker J (2009) An EKF assimilation of AMSR-E soil moisture into the ISBA land surface scheme. J Geophys Res 114:D20104.1–D20104.13. doi: 10.1029/2008JD011650 CrossRefGoogle Scholar
  79. Draper C, Mahfouf J-F, Walker J (2011) Root zone soil moisture from the assimilation of screen-level variables and remotely sensed soil moisture. J Geophys Res 116:D02127Google Scholar
  80. Draper CS, Reichle RH, De Lannoy GJM, Liu Q (2012) Assimilation of passive and active microwave soil moisture retrievals. Geophys Res Lett 39:L04401Google Scholar
  81. Drusch M (2007) Initializing numerical weather prediction models with satellite surface soil moisture: Data assimilation experiments with ECMWF’s integrated forecast system and the TMI soil moisture data set. J Geophys Res 112. doi: 10.1029/2006JD007478
  82. Drusch M, Viterbo P (2007) Assimilation of screen-level variables in ECMWF’s integrated forecast system: a study on the impact on the forecast quality and analyzed soil moisture. Mon Weather Rev 135:300–314Google Scholar
  83. Drusch M, Vasilievic D, Viterbo P (2004) ECMWF’s global snow analysis: assessment and revision based on satellite observations. J Appl Meteorol 43:1282–1294Google Scholar
  84. Drusch M, Wood EF, Gao H (2005) Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture. J Geophys Res 32:L15403.1–L15403.4Google Scholar
  85. Duan Q, Sorooshian S, Gupta VK (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28:1015–1031Google Scholar
  86. Dumedah G, Berg AA, Wineberg M (2011) An integrated framework for a joint assimilation of brightness temperature and soil moisture using the nondominated sorting genetic algorithm II. J Hydrometeorol 12:1596–1609Google Scholar
  87. Dumont M, Durand Y, Arnaud Y, Six D (2012) Variational assimilation of albedo in a snowpack model and reconstruction of the spatial mass-balance distribution of an alpine glacier. J Glaciol 58:151–164Google Scholar
  88. Dunne S, Entekhabi D (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–W01407.15Google Scholar
  89. Durand M, Margulis SA (2006) Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent. J Hydrometeorol 7:443–457Google Scholar
  90. Durand M, Margulis SA (2007) Correcting first-order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme. J Geophys Res 112:D13121.1–D13121.15Google Scholar
  91. Durand M, Andreadis K, Alsdorf D, Lettenmaier D, Moller D, Wilson M (2008) Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model. Geophys Res Lett 35:L20401Google Scholar
  92. Durand M, Kim E, Margulis SA (2009) Radiance assimilation shows promise for snowpack characterization. Geophys Res Lett 36:L02503.1–L02503.5Google Scholar
  93. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res 108:8851. doi: 10.1029/2002JD003296.0 CrossRefGoogle Scholar
  94. Entekhabi D, Nakamura H, Njoku EG (1994) Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE Trans Geosci Remote Sens 32:438–448Google Scholar
  95. Entekhabi D, Njoku EG, O’Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J, Kimball J, Piepmeier JR, Koster RD, Martin N, McDonald KC, Moghaddam M, Moran S, Reichle R, Shi JC, Spencer MW, Thurman SW, Tsang L, Van Zyl J (2010a) The Soil Moisture Active and Passive (SMAP) mission. Proc IEEE 98:704–716Google Scholar
  96. Entekhabi D, Reichle RH, Koster RD, Crow WT (2010b) Performance metrics for soil moisture retrievals and application requirements. J Hydrometeorol 11:832–840Google Scholar
  97. Essery R, Pomeroy J (2004) Implications of spatial distributions of snow mass and melt rate for snow-cover depletion: theoretical considerations. Ann Glaciol 38:261–265Google Scholar
  98. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367Google Scholar
  99. Famiglietti J, Wood E (1994) Multiscale modeling of spatially variable water and energy balance processes. Water Resour Res 30:3061–3078Google Scholar
  100. Flores A, Bras RL, Entekhabi D (2012) Hydrologic data assimilation with a hillslope-scale resolving model and L-band radar observations: synthetic experiments with the ensemble Kalman filter. Water Resour Res 48:W08509Google Scholar
  101. Forman BA, Reichle RH, Rodell M (2012) Assimilation of terrestrial water storage from GRACE in a snow-dominated basin. Water Resour Res 48:W01507Google Scholar
  102. Forman B, Reichle RH, Derksen C (2013) Estimating passive microwave brightness temperature over snowcovered land in North America using a land surface model and an artificial neural network. IEEE Trans Geosci Remote Sens (in press)Google Scholar
  103. Francois C, Quesney A, Ottlé C (2003) Sequential assimilation of ERS-1 SAR data into a coupled land surface-hydrological model using an extended Kalman filter. J Hydrometeorol 4:473–487Google Scholar
  104. Galantowicz JF, Entekhabi D, Njoku EG (1999) Tests of sequential data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness. IEEE Trans Geosci Remote Sens 37:1860–1870Google Scholar
  105. GCOS-107 (2006) Systematic observation requirements for satellite-based products for climate. Supplemental details to the satellite-based component of the “implementation plan for the global observing system for climate in support of the UNFCCC”, GCOS-107, WMO/TD No. 1338, Sept 2006Google Scholar
  106. Georgakakos KP, Baumer OW (1996) Measurement and utilization of on-site soil moisture data. J Hydrol 184:131–152Google Scholar
  107. Ghent D, Kaduk J, Remedios J, Ard J, Balzter H (2010) Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter. J Geophys Res 115:D19112Google Scholar
  108. Giustarini L, Matgen P, Hostache R, Montanari M, Plaza D, Pauwels V, De Lannoy G, De Keyser R, Pfister L, Hoffmann L, Savenije H (2011) Assimilating SAR-derived water level data into a flood models: a case study. Hydrol Earth Syst Sci 15:2349–2365Google Scholar
  109. Günter A (2008) Improvement of global hydrological models using GRACE data. Surv Geophys 2008:375–397Google Scholar
  110. Guo JCY (2006) Urban hydrology and hydraulic design. Water Resources Publications, LLC, ColoradoGoogle Scholar
  111. Gustafsson D et al (2004) Modeling water and heat balance of the Boreal landscape—comparison of forest and arable land in Scandinavia. J Appl Meteorol 43:1750–1767Google Scholar
  112. Gutmann E, Small E (2010) A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land-surface models. Water Resour Res 46:W06520Google Scholar
  113. Han E, Merwade V, Heathman G (2012a) Application of data assimilation with the root zone water quality model for soil moisture profile estimation in the upper Cedar Creek, Indiana. Hydrol Process 26:1707–1719Google Scholar
  114. Han X, Li X, Hendricks H, Vereecken H, Montzka C (2012b) Spatial horizontal correlation characteristics in the land data assimilation of soil moisture. Hydrol Earth Syst Sci 26:1349–1363Google Scholar
  115. He M, Hogue T, Margulis S, Franz K (2012) An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions. Hydrol Earth Syst Sci 16:815–831Google Scholar
  116. Hoeben R, Troch PA (2000) Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour Res 36:2805–2819Google Scholar
  117. Houser PR (2003) Land data assimilation systems. In: Swinbank R, Shuytaev V, Lahoz WA (eds) Data assimilation for the earth system. NATO science series: IV: earth and environmental sciences, vol 26. Kluwer, Dordrecht, pp 345–360Google Scholar
  118. Houser PR, Shuttleworth WJ, Famiglietti JS, Gupta HV, Syed KH, Goodrich DC (1998) Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour Res 34:3405–3420Google Scholar
  119. Houser PR, De Lannoy GJM, Walker JP (2010) Land surface data assimilation. In: Lahoz WA, Khattatov B, Ménard R (eds) Data assimilation: making sense of observations. Springer, Berlin, pp 549–597Google Scholar
  120. Hurkmans R, Paniconi C, Troch PA (2006) Numerical assessment of a dynamical relaxation data assimilation scheme for a catchment hydrological model. Hydrol Process 20:549–563Google Scholar
  121. Ines A, Mohanty B (2009) Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02. Water Resour Res 45:W01408Google Scholar
  122. IPCC (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report, Intergovernmental Panel on Climate Change, Cambridge University Press, CambridgeGoogle Scholar
  123. Jackson TJ, Bindlish R, Cosh MH, Zhao T, Starks PJ, Bosch DD, Seyfried M, Moran MS, Goodrich DC, Kerr YH, Leroux D (2012) Validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Trans Geosci Remote Sens 50:1530–1543Google Scholar
  124. Jansson P-E, Karlberg L (2004) CoupModel, coupled heat and mass transfer model for soil-plant-atmosphere system. Royal Institute of Technology (web-based on line documentation with details on use of model). http://www.lwr.kth.se/Vara%20Datorprogram/CoupModel/index.htm
  125. Jansson C et al (2005) Preferential water flow in a glacial till soil. Nord Hydrol 36:1–11Google Scholar
  126. Jansson P-E et al (2008) Simulated climate change impacts on fluxes of carbon in Norway spruce ecosystems along a climatic transect in Sweden. Biogeochemistry 89:81–94Google Scholar
  127. Jarlan L, Balsamo G, Lafont S, Beljaars A, Calvet J-C, Mougin E (2008) Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: application to West Africa. J Geophys Res 113:D24117Google Scholar
  128. Jones A, Vukićević T, Vonder Haar T (2003) Variational data assimilation of soil moisture using 6 and 10 GHz passive microwave data. J Hydrometeorol 5:213–229Google Scholar
  129. Kainulainen J, Colliander A, Closa J, Martin-Neira M, Oliva R, Buenadicha G, Rubiales Alcaine P, Hakkarainen A, Hallikainen MT (2012) Radiometric performance of the SMOS reference radiometers—assessment after one year of operation. IEEE Trans Geosci Remote Sens 50:1367–1383Google Scholar
  130. Kalma J, McVicar T, McCabe M (2008) Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv Geophys 29:421–469Google Scholar
  131. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, CambridgeGoogle Scholar
  132. Kalnay E (2010) Ensemble Kalman filter: current status and potential. In: Lahoz WA, Khattatov B, Ménard R (eds) Data assimilation: making sense of observations. Springer, Berlin, pp 69–92Google Scholar
  133. Karlberg L et al (2006) Modelling transpiration and growth in salinity-stressed tomato under different climatic conditions. Ecol Model 190:15–40Google Scholar
  134. Karlberg L et al (2007) Modeling carbon turnover in five terrestrial ecosystems in the boreal zone using multiple criteria of acceptance. AMBIO J Hum Environ 35:448–458Google Scholar
  135. Kerr YH, Waldteufel P, Wigneron J-P, Delwart S, Cabot F, Boutin J, Escorihuela M-J, Font J, Reul N, Gruhier C, Juglea SE, Drinkwater MR, Hahne A, Martín-Neira M, Mecklenburg S (2010) The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc IEEE 98:666–687Google Scholar
  136. Kerr YH, Font J, Martin-Neira M, Mecklenburg S (2012a) Introduction to the special issue on the ESA’s Soil Moisture and Ocean Salinity Mission (SMOS)—instrument performance and first results. IEEE Trans Geosci Remote Sens 50:1351–1353Google Scholar
  137. Kerr YH, Waldteufel P, Richaume P, Wigneron JP, Ferrazzoli P, Mahmoodi A, Al Bitar A, Cabot F, Gruhier C, Juglea SE, Leroux D, Mialon A, Delwart S (2012b) The SMOS soil moisture retrieval algorithm. IEEE Trans Geosci Remote Sens 50:1384–1403Google Scholar
  138. Klemedtsson L et al (2008) Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil. Biogeochemistry 89:61–79Google Scholar
  139. Kolberg S, Gottschalk L (2010) Interannual stability of grid cell snow depletion curves as estimated from MODIS images. Water Resour Res 46:W11555Google Scholar
  140. Koster RD, Suarez MJ, Ducharne A, Stieglitz M, Kumar P (2000) A catchment-based approach to modeling land surface processes in a general circulation model 1. Model structure. J Geophys Res 105:24809–24822Google Scholar
  141. Koster RD, Dirmeyer PA, Guo Z, Bonan G, Cox P, Gordon C, Kanae S, Kowalczyk E, Lawrence D, Liu P, Lu C, Malyshev S, McAvaney B, Mitchell K, Mocko D, Oki T, Oleson K, Pitman A, Sud Y, Taylor C, Verseghy D, Vasic R, Xue Y, Yamada T (2004a) Regions of strong coupling between soil moisture and precipitation. Science 305:1138–1140Google Scholar
  142. Koster RD, Suarez M, Liu P, Jambor U, Berg A, Kistler M, Reichle R, Rodell M, Famiglietti J (2004b) Realistic initialization of land surface states: impacts on subseasonal forecast skill. J Hydrometeorol 5:1049–1063Google Scholar
  143. Koster RD, Mahanama PP, Yamada TJ, Balsamo G, Berg AA, Boisserie M, Dirmeyer PA, Doblas-Reyes FJ, Drewitt G, Gordon CT, Guo Z, Jeong JH, Lee WS, Li Z, Luo L, Malyshev S, Merryfield WJ, Seneviratne SI, Stanelle T, van den Hurk BJJM, Vitart F, Wood EF (2011) The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J Hydrometeorol 12:805–822Google Scholar
  144. Kotecha JH, Djurić PM (2003) Gaussian particle filtering. IEEE Trans Signal Process 51:2592–2601Google Scholar
  145. Kumar SV, Reichle RH, Peters-Lidard CD, Koster RD, Zhan X, Crow WT, Eylander JB, Houser PR (2008) A land surface data assimilation framework using the Land Information System: description and applications. Adv Water Resour 31:1419–1432Google Scholar
  146. Kumar S, Reichle RH, Koster RD, Crow WT, Peters-Lidard CD (2009) Role of subsurface physics in the assimilation of surface soil moisture observations. J Hydrometeorol 10:1534–1547. doi: 10.1109/MC.2008.511 CrossRefGoogle Scholar
  147. Kumar S, Reichle R, Harrison K, Peters-Lidard C, Yatheendradas S, Santanello J (2012) A comparison of methods for a priori bias correction in soil moisture data assimilation. Water Resour Res 48:W03515Google Scholar
  148. Lacava T, Matgen P, Brocca L, Bittelli M, Pergola N, Moramarco T, Tramutoli V (2012) A first assessment of the SMOS soil moisture product with in situ and modeled data in Italy and Luxembourg. IEEE Trans Geosci Remote Sens 50:1612–1622Google Scholar
  149. Lahoz WA, Khattatov B, Ménard R (eds) (2010a) Data assimilation: making sense of observations. Springer, BerlinGoogle Scholar
  150. Lahoz WA, Walker S-E, Dammann D (2010b) The NILU SURFEX-EnKF land data assimilation system. NILU technical report TR 2/2010, Jan 2010. Available from http://www.nilu.no
  151. Lakshmi V (2000) A simple surface temperature assimilation scheme for use in land surface models. Water Resour Res 36:3687–3700Google Scholar
  152. Le Moigne P (2009) SURFEX scientific documentation. Note de Centre No. 87, Groupe de Meteorologie a Moyenne Exhell, Centre National de Recherches Météorologiques, Météo-France (SURFEX, V. 5, Issue 1). Available from http://hirlam.org/index.php?option=com_docman&task=doc_downloads&gid=605&Itemid=70
  153. Le Vine DM, Lagerloef GSE, Yueh S, Pellerano F, Dinnat E, Wentz F (2006) Aquarius mission technical overview. IGARSS 2006, pp 1678–1680Google Scholar
  154. Lee H, Seo D, Koren V (2011) Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: effects of uncertainties in the data and initial model soil moisture states. Adv Water Resour 34:1597–1615Google Scholar
  155. Lewis P, Gómez-Dans J, Kaminski T, Settle J, Quaife T, Gobron N, Styles J, Berger M (2012) An earth observation land data assimilation system (EO-LDAS). Remote Sens Environ 120:219–235Google Scholar
  156. Li J, Islam S (1999) On the estimation of soil moisture profile and surface fluxes partitioning from sequential assimilation of surface layer soil moisture. J Hydrol 220:86–103Google Scholar
  157. Li B, Rodell M, Zaitchik BF, Reichle RH, Koster RD, van Dam TM (2012) Assimilation of GRACE terrestrial water storage into a land surface model: evaluation and potential value for drought monitoring in western and central Europe. J Hydrol 446–447:103–115Google Scholar
  158. Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for GSMs. J Geophys Res 99:14415–14428Google Scholar
  159. Liston G, Hiemstra CA (2008) A simple data assimilation system for complex snow distributions (SnowAssim). J Hydrometeorol 9:989–1004Google Scholar
  160. Liu Q, Reichle RH, Bindlish R, Cosh MH, Crow WT, de Jeu R, De Lannoy GJM, Huffman GJ, Jackson TJ (2011) The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J Hydrometeorol 12:750–765Google Scholar
  161. Lo M, Famiglietti JS, Yeh PJ, Syed TH (2010) Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data. Water Resour Res 46:W05517Google Scholar
  162. Loew A, Mauser W (2008) Inverse modeling of soil characteristics from surface soil moisture observations: potential and limitations. Hydrol Earth Syst Sci 5:95–145Google Scholar
  163. Loew A, Schwank M, Schlenz F (2009) Assimilation of an L-band microwave soil moisture proxy to compensate for uncertainties in precipitation data. TGRS 47:2606–2616Google Scholar
  164. Loth B, Graf H-F, Oberhuber JM (1993) Snow cover model for global climate simulations. J Geophys Res 98:10451–10464Google Scholar
  165. Lynch-Stieglitz M (1994) The development and validation of a simple snow model for the GISS GCM. J Clim 7:1842–1855Google Scholar
  166. Mackaro S, McNider R, Pour Biazar A (2011) Some physical and computational issues in land surface data assimilation of satellite skin temperatures. Pure Appl Geophys 167:1303–1458Google Scholar
  167. McLaughlin D (2002) An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering. Adv Water Resour 25:1275–1286Google Scholar
  168. Madsen H (2003) Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Resour 26:205–216Google Scholar
  169. Maggioni V, Reichle R, Emmanouil A (2011) The effect of satellite rainfall error modeling on soil moisture prediction uncertainty. J Hydrometeorol 12:413–428Google Scholar
  170. Mahfouf J-F (2010) Assimilation of satellite-derived soil moisture from ASCAT in a limited-area NWP model. Q J R Meteorol Soc 136:784–798Google Scholar
  171. Mahfouf J-F, Bliznak V (2011) Combined assimilation of screen-level observations and radar-derived precipitation for soil moisture analysis. Q J R Meteorol Soc 137:709–722Google Scholar
  172. Mahfouf J-F, Noilhan J (1996) Inclusion of gravitational drainage in a land surface scheme based on the force restore method. J Appl Meteorol 35:987–992Google Scholar
  173. Mahfouf J-F, Viterbo P, Douville H, Beljaars A, Saarinen S (2000) A revised land surface analysis scheme in the integrated forecasting system. ECMWF Newsl 88:8–13Google Scholar
  174. Mahfouf J-F, Bergaoui K, Draper C, Bouyssel C, Taillefer F, Taseva L (2009) A comparison of two off-line soil analysis schemes for assimilation of screen-level observations. J Geophys Res 114:D08105Google Scholar
  175. Margulis SA, McLaughlin D, Entekhabi D, Dunne S (2002) Land data assimilation of soil moisture using measurements from the Southern Great Plains 1997 field experiment. Water Resour Res 38:35.1–35.18Google Scholar
  176. Marzahn P, Ludwig R (2009) On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modeling. Hydrol Earth Syst Sci 13:381–394Google Scholar
  177. Matgen P, Montanari M, Hostache R, Pfister L, Hoffmann L, Plaza D, Pauwels V, De Lannoy G, De Keyser R, Savenije H (2010) Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the particle filter: proof of concept. Hydrol Earth Syst Sci 14:1773–1785Google Scholar
  178. Mattar C, Wigneron J-P, Sobrino JA, Novello N, Calvet JC, Albergel C, Richaume P, Mialon A, Guyon D, Jimenez-Munoz JC, Kerr Y (2012) A combined optical-microwave method to retrieve soil moisture over vegetated areas. IEEE Trans Geosci Remote Sens 50:1404–1413Google Scholar
  179. Mecklenburg S, Drusch M, Kerr YH, Font J, Martin-Neira M, Delwart S, Buenadicha G, Reul N, Daganzo-Eusebio E, Oliva R, Crapolicchio R (2012) ESA’s Soil Moisture and Ocean Salinity Mission: mission performance and operations. IEEE Trans Geosci Remote Sens 50:1354–1366Google Scholar
  180. Ménard R (2010) Bias estimation. In: Lahoz WA, Khattatov B, Ménard R (eds) Data assimilation: making sense of observations. Springer, Berlin, pp 113–135Google Scholar
  181. Meng CL, Li Z-L, Zhan X, Shi JC, Liu CY (2009) Land surface temperature data assimilation and its impact on evapotranspiration estimates from the common land model. Water Resour Res 45:W02421Google Scholar
  182. Merlin O, Rüdiger C, Al Bitar A, Richaume P, Walker JP, Kerr YH (2012) Disaggregation of SMOS soil moisture in Southeastern Australia. IEEE Trans Geosci Remote Sens 50:1556–1571Google Scholar
  183. Mialon A, Wigneron J-P, de Rosnay P, Escorihuela MJ, Kerr YH (2012) Evaluating the L-MEB model from long-term microwave measurements over a rough field, SMOSREX 2006. IEEE Trans Geosci Remote Sens 50:1458–1467Google Scholar
  184. Misra S, Ruf CS (2012) Analysis of radio frequency interference detection algorithms in the angular domain for SMOS. IEEE Trans Geosci Remote Sens 50:1448–1457Google Scholar
  185. Monsivais-Huerteroet A, Graham W, Judge J, Agrawal D (2010) Effect of simultaneous state-parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKf. Adv Water Resour 33:468–484Google Scholar
  186. Montanari A, Toth E (2007) Calibration of hydrological models in the spectral domain: an opportunity for scarcely gauged basins? Water Resour Res 43:W05434Google Scholar
  187. Montanari M, Hostache R, Matgen P, Schumann G, Pfister L, Hoffmann L (2009) Calibration and sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water stages. Hydrol Earth Syst Sci 13:367–380Google Scholar
  188. Montzka C, Grant J, Moradkhani H, Hendricks-Franssen H-J, Weihermuller L, Drusch M, Vereecken H (2012) Estimation of radiative transfer parameters from SMOS brightness temperatures using data assimilation: implication on soil moisture retrieval. Vadose Zone J (submitted)Google Scholar
  189. Moradkhani H (2008) Hydrologic remote sensing and land surface data assimilation. Sensors 8:2986–3004Google Scholar
  190. Moradkhani H, Hsu K-L, Gupta H, Sorooshian S (2005a) Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resour Res 41:W05012. doi: 10.1029/2004WR003604 CrossRefGoogle Scholar
  191. Moradkhani H, Sorooshian S, Gupta HV, Houser PR (2005b) Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv Water Resour 28:135–147Google Scholar
  192. Morisette JT, Privette JL, Justice CO (2002) A framework for the validation of MODIS land products. Remote Sens Environ 83:77–96Google Scholar
  193. Nagarajanar K, Judgea J, Graham WD, Monsivais-Huerteroet A (2011) Particle filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions. Adv Water Resour 34:433–447Google Scholar
  194. Nearing GS, Moran MS, Thorp KR, Collins CDH, Slack DC (2010) Likelihood parameter estimation for calibrating a soil moisture model using radar backscatter. Remote Sens Environ 115:2564–2574Google Scholar
  195. Nearing GS, Crow WT, Thorp KR, Moran MS, Reichle RH, Gupta HV (2012) Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: an observing system simulation experiment. Water Resour Res 48:W05525Google Scholar
  196. Nichols NK (2010) Mathematical concepts of data assimilation. In: Lahoz WA, Khattatov B, Ménard R (eds) Data assimilation: making sense of observations. Springer, Berlin, pp 13–39Google Scholar
  197. Ni-Meister W (2008) Recent advances on soil moisture data assimilation. Phys Geogr 29:19–37Google Scholar
  198. Ni-Meister W, Houser PR, Walker J (2006) Soil moisture initialization for climate prediction: assimilation of scanning multifrequency microwave radiometer soil moisture data into a land surface model. J Geophys Res 111:1–15. doi: 10.1029/2006JD007190 CrossRefGoogle Scholar
  199. Njoku EG, Chan TK (2006) Vegetation and surface roughness effects on AMSR-E land observations. Remote Sens Environ 100:190–199Google Scholar
  200. Noilhan J, Mahfouf J-F (1996) The ISBA land surface parameterisation scheme. Global Planet Chang 13:145–159Google Scholar
  201. Norman J et al (2008) Simulation of NO and N2O emissions from a spruce forest during a freeze/thaw event using an N-flux sub-model from the PnET-N-DNDC model integrated to CoupModel. Ecol Model 216:18–30Google Scholar
  202. Oki T, Kanae S (2006) Global hydrological cycles and world water resources. Science 313:1068–1072Google Scholar
  203. Oleson KW et al (2010) Technical description of version 4.0 of the Community Land Model (CLM), NCAR technical note NCAR/TN-478+STR, 257 ppGoogle Scholar
  204. Oliva R, Daganzo E, Kerr YH, Mecklenburg S, Nieto S, Richaume P, Gruhier C (2012) SMOS radio frequency interference scenario: Status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band. IEEE Trans Geosci Remote Sens 50:1427–1439Google Scholar
  205. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteorol Soc 89:459–470Google Scholar
  206. Pan M, Wood EF (2006) Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J Hydrometeorol 7:534–547Google Scholar
  207. Pan M, Wood EF (2010) Impact of accuracy, spatial availability, and revisit time of satellite-derived surface soil moisture in a multiscale ensemble data assimilation system. J Sel Top Appl Earth Obs Remote Sens 3:49–56Google Scholar
  208. Pan M, Wood EF, Wójcik R, McCabe MF (2008) Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation. Remote Sens Environ 112:1282–1294Google Scholar
  209. Pan M, Wood EF, McLaughlin DB, Entekhabi D, Luo L (2009) A multiscale ensemble filtering system for hydrologic data assimilation: part I, implementation and synthetic experiment. J Hydrometeorol 10:807–819Google Scholar
  210. Paniconi C, Marrocu M, Putti M, Verbunt M (2003) Newtonian nudging for a Richards equation-based distributed hydrological model. Adv Water Resour 26:161–178Google Scholar
  211. Parada LM, Liang X (2008) Impacts of spatial resolutions and data quality on soil moisture data assimilation. J Geophys Res 113:D10101.1–D10101.17Google Scholar
  212. Parajka J, Naeimi V, Blöschl G, Wagner W, Merz R, Scipal K (2006) Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. Hydrol Earth Syst Sci 10:353–368Google Scholar
  213. Parinussa R, Holmes TRH, Yilmaz MT, Crow WT (2011) The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations. Hydrol Earth Syst Sci 15:3135–3151Google Scholar
  214. Pathmathevan M, Koike T, Lin X, Fujii H (2003) A simplified land data assimilation scheme and its application to soil moisture experiments in 2002 (SMEX02). Water Resour Res 39:SWC6.1–SWC6.20Google Scholar
  215. Pauwels VRN, De Lannoy GJM (2006) Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge. J Hydrometeorol 7:458–477Google Scholar
  216. Pauwels VRN, De Lannoy GJM (2009) Ensemble-based assimilation of discharge into rainfall-runoff models: a comparison of approaches to mapping observational information to state space. Water Resour Res 45:W08428.1–W08428.17Google Scholar
  217. Pauwels VRN, Hoeben R, Verhoest NEC, De Troch FP (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 251:88–102Google Scholar
  218. Pauwels VRN, Hoeben R, Verhoest NEC, De Troch FP, Troch PA (2002) Improvement of TOPLATS-based discharge predictions through assimilation of ERS-based remotely sensed soil moisture values. Hydrol Process 16:995–1013Google Scholar
  219. Pauwels VRN, Verhoest NEC, De Lannoy GJM, Defourny P, Guissard V, Lucau C (2006) 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:W04421.1–W04421.17Google Scholar
  220. Pauwels V, Balenzano A, Satalino G, Skriver H, Verhoest N, Mattia F (2009) Optimization of soil hydraulic model parameters using synthetic aperture radar data: An integrated multidisciplinary approach. IEEE Trans Geosci Remote Sens 47:455–457Google Scholar
  221. Peischl S, Walker JP, Rüdiger C, Ye N, Kerr YH, Kim E, Bandara R, Allahmoradi M (2012a) The AACES field experiments: SMOS calibration and validation across the Murrumbidgee River catchment. Hydrol Earth Syst Sci 16:1697–1708Google Scholar
  222. Peischl S, Walker JP, Ryu D, Kerr YH, Panciera R, Rüdiger C (2012b) Wheat canopy structure and surface roughness effects on multiangle observations at L-band. IEEE Trans Geosci Remote Sens 50:1498–1506Google Scholar
  223. Pipunic RC, Walker J, Western A (2008) Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: a comparative synthetic study. Remote Sens Environ 112:1295–1305Google Scholar
  224. Plaza DA, De Keyser R, De Lannoy GJM, Giustarini L, Matgen P, Pauwels VRN (2012) The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter. Hydrol Earth Syst Sci 16:375–390Google Scholar
  225. Quets J, De Lannoy G, Pauwels V (2010) Analysis of spectral calibration methods for precipitation-discharge processes. Hydrol Process 24:1048–1062Google Scholar
  226. Rautiainen K, Lemmetyinen J, Pulliainen J, Vehvilainen J, Drusch M, Kontu A, Kainulainen J, Seppanen J (2012) L-band radiometer observations of soil processes in boreal and subarctic environments. IEEE Trans Geosci Remote Sens 50:1483–1497Google Scholar
  227. Reichle RH (2008) Data assimilation methods in the earth sciences. Adv Water Resour 31:1411–1418Google Scholar
  228. Reichle RH, Koster R (2003) Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J Hydrometeorol 4:1229–1242Google Scholar
  229. Reichle RH, Koster R (2004) Bias reduction in short records of satellite soil moisture. Geophys Res Lett 31:L19501.1–L19501.4Google Scholar
  230. Reichle RH, Koster R (2005) Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys Res Lett 32:L0204.1–L0204.4Google Scholar
  231. Reichle RH, Entekhabi D, McLaughlin DB (2001a) Downscaling of radio brightness measurements for soil moisture estimation: a four dimensional variational data assimilation approach. Water Resour Res 37:2353–2364Google Scholar
  232. Reichle RH, McLaughlin DB, Entekhabi D (2001b) Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications. IEEE Trans Geosci Remote Sens 39:1708–1718Google Scholar
  233. Reichle RH, McLaughlin DB, Entekhabi D (2002a) Hydrologic data assimilation with the ensemble Kalman filter. Mon Weather Rev 120:103–114Google Scholar
  234. Reichle RH, Walker JP, Houser PR, Koster RD (2002b) Extended versus ensemble Kalman filtering for land data assimilation. J Hydrometeorol 3:728–740Google Scholar
  235. Reichle RH, Koster R, Liu P, Mahanama SPP, Njoku EG, Owe M (2007) Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J Geophys Res 112:D09108.1–D09108.14Google Scholar
  236. Reichle RH, Crow WT, Keppenne CL (2008) An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resour Res 44:W03423. doi: 10.1029/2007WR006357 CrossRefGoogle Scholar
  237. Reichle R, Bosilovich MG, Crow WT, Koster RD, Kumar SV, Mahanama SPP, Zaitchik BF (2009) Recent advances in land data assimilation at the NASA Global Modeling and Assimilation Office. In: Park SK, Xu L (eds) Data assimilation for atmospheric, oceanic and hydrologic applications. Springer, New York, pp 407–428Google Scholar
  238. Reichle R, Kumar SV, Mahanama SPP, Koster RD, Liu Q (2010) Assimilation of satellite derived skin temperature observations into land surface models. J Hydrometeorol 11. doi: 10.1175/2010JHM1262.1 Google Scholar
  239. Reichle RH, Koster RD, De Lannoy GJM, Forman BA, Liu Q, Mahanama SPP, Toure A (2011) Assessment and enhancement of MERRA land surface hydrology estimates. J Clim 24:6322–6338Google Scholar
  240. Reichle R, Crow W, Koster R, Kimball J, De Lannoy G (2012) SMAP Algorithm Theoretical Basis Document: L4 surface and root zone soil moisture product. Tech. Rep. SMAP Project, JPL D-66483, Jet PropulsionLaboratory, Pasadena, CA.Google Scholar
  241. Reichle R, De Lannoy GJM, Forman B, Draper C, Liu Q (2013) Connecting satellite observations with water cycle variables through land data assimilation: examples using the NASA GEOS-5 LDAS. Surv Geophys (in press)Google Scholar
  242. Renzullo LJ, Barrett DJ, Marks AS, Hill MJ, Guerschman JP, Mu Q, Running SW (2008) Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters. Remote Sens Environ 112:1308–1319Google Scholar
  243. Rodell M, Houser P (2004) Updating a land surface model with MODIS-derived snow cover. J Hydrometeorol 5:1064–1075Google Scholar
  244. Rodgers CD (2000) Inverse methods for atmospheric sounding: theory and practice. World Scientific Publishing Co. Ltd, LondonGoogle Scholar
  245. Rowlandson TL, Hornbuckle BK, Bramer LM, Patton JC, Logsdon SD (2012) Comparisons of evening and morning SMOS passes over the Midwest United States. IEEE Trans Geosci Remote Sens 50:1544–1555Google Scholar
  246. Roy A, Royer A, Turcotte R (2010) Improvement of springtime streamflow simulations in a boreal environment by incorporating snow-covered area derived from remote sensing data. J Hydrol 390:35–44Google Scholar
  247. Rüdiger C, Albergel C, Mahfouf J-F (2010) Evaluation of the observation operator Jacobian for leaf area index data assimilation with an extended Kalman filter. J Geophys Res 118:D09111Google Scholar
  248. Sabater JM, Jarlan L, Calvet J-C, Bouyssel F (2007) From near-surface to root-zone soil moisture using different assimilation techniques. J Hydrometeorol 8:194–206Google Scholar
  249. Saha S, Moorthi S, Pan H-L, Wu X et al (2010) The NCEP climate forecast system re-analysis. Bull Am Metereol Soc 91:1015–1057Google Scholar
  250. Sahoo A, De Lannoy GJM, Reichle RH, Houser PR (2013) Assimilation and downscaling of satellite observed soil moisture over the Little River experimental watershed in Georgia, USA. Adv Water Resour 52:19–33. doi: 10.1016/j.advwatres.2012.08.007 CrossRefGoogle Scholar
  251. Sanchez N, Martinez-Fernandez J, Scaini A, Perez-Gutierrez C (2012) Validation of the SMOS L2 soil moisture data in the REMEDHUS network (Spain). IEEE Trans Geosci Remote Sens 50:1602–1611Google Scholar
  252. Santanello J, Peters-Lidard C, Garcia ME, Mocko DM, Tischler MA, Moran MS, Thoma DP (2007) Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed. Remote Sens Environ 110:79–97Google Scholar
  253. Schlenz F, dall’Amico JT, Loew A, Mauser W (2012) Uncertainty assessment of the SMOS validation in the Upper Danube catchment. IEEE Trans Geosci Remote Sens 50:1517–1529Google Scholar
  254. Schuurmans J, Troch P, Veldhuizen A, Bastiaansen W, Bierkens M (2003) Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv Water Resour 26:151–159Google Scholar
  255. Schwank M, Wigneron J-P, Lopez-Baeza E, Volksch I, Matzler C, Kerr YH (2012) L-band radiative properties of vine vegetation at the MELBEX III SMOS cal/val site. IEEE Trans Geosci Remote Sens 50:1587–1601Google Scholar
  256. Scipal K, Holmes T, de Jeu R, Naeimi V, Wagner W (2008) A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophys Res Lett 35:L24403. doi: 10.1029/2008GL035599 CrossRefGoogle Scholar
  257. Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B et al (2010) Investigating soil moisture-climate interactions in a changing climate: a review. Earth Sci Rev 99:125–161Google Scholar
  258. Seo D-J, Cajina L, Corby R, Howieson T (2009) Automatic state updating for operational streamflow forecasting via variational data assimilation. J Hydrol 367:255–275Google Scholar
  259. Seuffert G, Wilker H, Viterbo P, Drusch M, Mahfouf J-F (2004) The usage of screen-level parameters and microwave brightness temperature for soil moisture analysis. J Hydrometeorol 5:516–531Google Scholar
  260. Sherwood SC (1999) Convective precursors and predictability in the Tropical Western Pacific. Mon Weather Rev 127:2977–2991Google Scholar
  261. Silvapalan M, Beven KJ, Wood EF (1987) On hydrologic similarity: 2. A scaled model of storm runoff production. Water Resour Res 23:2266–2278Google Scholar
  262. Simmons AJ, Hollingsworth A (2002) Some aspects of the improvement in skill of numerical weather prediction. Q J R Meteorol Soc 128:647–677Google Scholar
  263. Sini F, Boni G, Caparrini F, Entekhabi D (2008) Estimation of large-scale evaporation fields based on assimilation of remotely sensed land temperature. Water Resour Res 44:W06410Google Scholar
  264. Slater AG, Clark M (2006) Snow data assimilation via an ensemble Kalman filter. J Hydrometeorol 7:478–493Google Scholar
  265. Spahni R, Wania R, Neef L et al (2010) Constraining global methane emissions and uptake by ecosystems. Biogeosci Discuss 8:1643–1665Google Scholar
  266. Stieglitz M, Rind D, Famiglietti J, Rosenzweig C (1997) An efficient approach to modeling the topographic control of surface hydrology for regional and global climate modeling. J Clim 10:118–137Google Scholar
  267. Su H, Yang Z, Niu G, Dickinson RE (2008) Enhancing the estimation of continental-scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter. J Geophys Res 113:D08120Google Scholar
  268. Su H, Yang Z, Dickinson RE, Wilson CR, Niu G-Y (2010) Multisensor snow data assimilation at continental scale: the value of Gravity Recovery and Climate Experiment terrestial water storage information. J Geophys Res 115:D10104.1–D10104.14Google Scholar
  269. Su H, Yang Z-L, Niu G-Y (2011) Parameter estimation in ensemble based snow data assimilation: A synthetic study. Adv Water Resour 34:407–416Google Scholar
  270. Sun S, Jin J, Xue Y (1999) A simple snow-atmosphere-soil transfer (SAST) model. J Geophys Res 104:19587–19597Google Scholar
  271. Sun C, Walker JP, Houser PR (2004) A methodology for snow data assimilation in a land surface model. J Geophys Res 109:D08108.1–D08108.12Google Scholar
  272. Svensson M et al (2008) Bayesian calibration of a model describing carbon, water and heat fluxes for a Swedish boreal forest stand. Ecol Model 213:331–344Google Scholar
  273. Taillefer F (2002) CANARI (code for the analysis necessary for arpege for its rejects and its initialization): Technical documentation, internal CNRM/GMAP report. Available from http://www.cnrm.meteo.fr/gmapdoc/spip.php?article3
  274. Talagrand O (2010a) Variational assimilation. In: Lahoz WA, Khattatov B, Ménard R (eds) Data assimilation: making sense of observations. Springer, Berlin, pp 41–67Google Scholar
  275. Talagrand O (2010b) Evaluation of assimilation algorithms. In: Lahoz WA, Khattatov B, Ménard R (eds) Data assimilation: making sense of observations. Springer, Berlin, pp 217–240Google Scholar
  276. Tapley BD, Bettadpur S, Ries JC, Thompson PF, Watkins MM (2004) GRACE measurements of mass variability in the earth system. Science 305:503–505Google Scholar
  277. Tedesco M, Reichle RH, Loew A, Markus T, Foster JL (2010) Dynamic approaches for snow depth retrieval from spaceborne microwave brightness temperature. IEEE Trans Geosci Remote Sens 48:1955–1967Google Scholar
  278. Trenberth KE, Fasullo J, Kiehl J (2009) Earth’s global energy budget. Bull Am Meteorol Soc 90:311–323Google Scholar
  279. USGEO (2010) Achieving and sustaining earth observations: a preliminary plan based on a strategic assessment by the U.S. Group on Earth Observations. Office of Science and Technology Policy, 69 pp. Available online at www.whitehouse.gov/sites/default/files/microsites/ostp/ostp-usgeo-reportearth-obs.pdf
  280. Vachon F, Goïta K, Séve DD, Royer A (2010) Inversion of a snow emission model calibrated with in situ data dor snow water equivalent monitoring. IEEE Trans Geosci Remote Sens 48:59–71Google Scholar
  281. Van den Hurk BJJ, Jia L, Jacobs C, Menenti M, Li Z-L (2002) Assimilation of land surface temperature data from ATSR in an NWP environment—a case study. Int J Remote Sens 23:5193–5209Google Scholar
  282. Van den Hurk B, Ettema J, Viterbo P (2008) Analysis of soil moisture changes in europe during a single growing season in a new ECMWF soil moisture assimilation system. J Hydrometeorol 9:116–131Google Scholar
  283. van Leeuwen PJ (2009) Particle filtering in geophysical systems. Mon Weather Rev 137:4089–4114Google Scholar
  284. Vereecken H, Huisman JA, Bogena H, Vanderborght J, Vrugt JA, Hopmans JW (2008) On the value of soil moisture measurements in vadose zone hydrology: a review. Water Resour Res 44:W00D06Google Scholar
  285. Vrugt JA, Gupta HV, Nualláin BÓ, Bouten W (2006) Real-time data assimilation for operational ensemble streamflow forecasting. J Hydrometeorol 7:548–565Google Scholar
  286. Vrugt J, ter Braak C, Clark M, Hyman J, Robinson B (2008) Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour Res 44:W00B09Google Scholar
  287. Vrugt J, ter Braak C, Diks C, Schoups G (2012) Advancing hydrologic data assimilation using particle markov chain monte carlo simulation: theory, concepts and applications. Adv Water Resour (anniversary issue—35 years). doi: 10.1016/j.advwatres.2012.04.002 Google Scholar
  288. Walker JP, Houser PR (2004) Requirements of a global near-surface soil moisture satellite mission: accuracy, repeat time, and spatial resolution. Adv Water Resour 27:785–801Google Scholar
  289. Walker JP, Willgoose GR, Kalma JD (2001a) One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms. Adv Water Resour 24:631–650Google Scholar
  290. Walker JP, Willgoose GR, Kalma JD (2001b) One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: a simplified soil moisture model and field application. J Hydrometeorol 2:356–373Google Scholar
  291. Walker JP, Willgoose GR, Kalma JD (2002) Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: simplified Kalman filter covariance forecasting and field application. Water Resour Res 38:37.1–37.13Google Scholar
  292. Wania R (2007) Modelling northern peatland land surface processes, vegetation dynamics and methane emissions. PhD thesis, University of Bristol, Bristol, 122 ppGoogle Scholar
  293. Weerts AH, El Serafy GYH (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 CrossRefGoogle Scholar
  294. Weisheimer A, Doblas-Reyes P, Jung T, Palmer T (2011) On the predictability of the extreme summer 2003 over Europe. Geophys Res Lett 38. doi: 10.1029/2010GL046455 Google Scholar
  295. Wilker H, Drusch M, Seuffert G, Simmer C (2006) Effects of the near-surface soil moisture profile on the assimilation of L-band microwave brightness temperature. J Hydrometeorol 7:433–442Google Scholar
  296. Wingeron J-P, Olioso A, Calvet J-C, Bertuzzi P (1999) Estimating root zone soil moisture from surface soil moisture data and soil–vegetation–atmosphere transfer modeling. Water Resour Res 35:3735–3745Google Scholar
  297. Xu T, Liang S, Liu S (2011) Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble kalman filter. J Geophys Res 116:621–639Google Scholar
  298. Yilmaz MT, Delsole T, Houser P (2011) Improving land data assimilation performance with a water budget constraint. J Hydrometeorol 12:1040–1055Google Scholar
  299. Zaitchik BF, Rodell M (2009) Forward-looking assimilation of MODIS-derived snow-covered area into a land surface model. J Hydrometeorol 10:130–148Google Scholar
  300. Zaitchik BF, Rodell M, Reichle RH (2008) Assimilation of GRACE terrestrial water storage data into a land surface model: results for the Mississippi river basin. J Hydrometeorol 9:535–548Google Scholar
  301. Zhan X, Houser PR, Walker JP, Crow WT (2006) A method for retrieving high-resolution surface soil moisture from Hydros L-band radiometer and radar observations. IEEE Trans Geosci Remote Sens 44:1534–1544Google Scholar
  302. Zhang S, Shi J, Dou Y (2011) A soil moisture assimilation scheme based on the microwave land emissivity model and the community land model. Int J Remote Sens 33:2770–2797Google Scholar
  303. Zupanski D (1997) A general weak constraint applicable to operational 4DVAR data assimilation systems. Mon Weather Rev 125:2274–2292Google Scholar

Copyright information

© The Author(s) 2013

Authors and Affiliations

  • William A. Lahoz
    • 1
    • 2
  • Gabriëlle J. M. De Lannoy
    • 3
  1. 1.NILUKjellerNorway
  2. 2.Météo-FranceCNRM/GMGEC/CARMAToulouseFrance
  3. 3.Global Modeling and Assimilation Office (Code 610.1)NASA/GSFCGreenbeltUSA

Personalised recommendations