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  • Betts, A. K., F. Chen, K. E. Mitchell, and Z. I. Janjic (1997), Assessment of the land surface and boundary layer models in two operational versions of the NCEP Eta Model using FIFE data, Mon. Weather Rev., 125, 28962916.
  • Beven, K. J., R. Lamb, P. F. Quinn, R. Romanowicz, and J. Freer (1995), TOPMODEL, in Computer Models of Watershed Hydrology, edited by V. P. Singh, pp. 627668, Water Resources Publications,, Highlands Ranch, Colo.,
  • Bonan, G. B. (1996), A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user's guide, NCAR Tech. Note NCAR/TN-417+STR, Natl. Cent. for Atmos. Res., Boulder, Colo.
  • Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. Zeng, Y. Dai, R. E. Dickinson, and Z.-L. Yang (2002), The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model, J. Clim., 15, 31233149.
  • Boone, A., et al. (2004), The Rhone-Aggregation Land Surface Scheme Intercomparison Project: An overview, J. Clim., 17, 187208.
  • Bosch, D. D., J. M. Sheridan, and F. M. Davis (1999), Rainfall characteristics and spatial correlation for the Georgia Coastal Plain, Trans. ASAE, 42(6), 16371644.
  • Bosch, D. D., V. Lakshmi, T. J. Jackson, M. Choi, and J. M. Jacobs (2006), Large scale measurements of soil moisture for validation of remotely sensed data: Georgia soil moisture experiment of 2003, J. Hydrol., 323, 120137.
  • Calvet, J. C., et al. (1999), MUREX: A land-surface field experiment to study the annual cycle of the energy and water budgets, Ann. Geophys., 17, 838854.
  • Campling, P., A. Gobin, K. Beven, and J. Feyen (2002), Rainfall-runoff modeling of a humid tropical catchment: The TOPMODEL approach, Hydrol. Process., 16, 231253.
  • Cashion, J., V. Lakshmi, D. Bosch, and T. J. Jackson (2005), Microwave remote sensing of soil moisture: Evaluation of the TRMM microwave imager (TMI) satellite for the Little River Watershed Tifton, Georgia, J. Hydrol., 307(1–4), 242253.
  • Chen, F., and J. Dudhia (2001), Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. part II: Preliminary model validation, Mon. Weather Rev., 129, 587604.
  • Chen, F., K. Mitchell, J. Schaake, Y. Xue, H.-L. Pan, V. Koren, Q. Duan, M. Ek, and A. Betts (1996), Modeling of land surface evaporation by four schemes and comparison with FIFE observations, J. Geophys. Res., 101, 72517266.
  • Clapp, R. B., and G. M. Hornberger (1978), Empirical equations for some soil hydraulic properties, Water Resour. Res., 14, 601604.
  • Cosgrove, B. A., et al. (2003), Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res., 108(D22), 8842, doi:10.1029/2002JD003118.
  • Dai, Y., and Q. Zeng (1997), A land surface model (IAP94) for climate studies. part I: formulation and validation in off-line experiments, Adv. Atmos. Sci., 14, 433460.
  • Dai, Y., et al. (2003), The Common Land Model, Bull. Am. Meteorol. Soc., 84, 10131023.
  • Deardorff, J. W. (1978), Efficient prediction of a ground surface temperature and moisture with inclusion of a layer of vegetation, J. Geophys. Res., 83, 18891903.
  • Dickinson, R. E., A. Henderson-Sellers, P. J. Kennedy, and M. F. Wilson (1986), Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR community climate model, NCAR Tech. Note, TN-275+STR, 69 pp., National Center for Atmospheric Research,, Boulder, Colo.
  • Dirmeyer, P. A. (1995), Problems in initializing soil wetness, Bull. Am. Meteorol. Soc., 76, 22342240.
  • Dirmeyer, P. A. (2004), Soil moisture—Muddy prospects for a clear definition, GEWEX News, 14(3), 1112.
  • Dirmeyer, P. A., A. J. Dolman, and N. Sato (1999), The Global Soil Wetness Project: A pilot project for global land surface modeling and validation, Bull. Am. Meteorol. Soc., 80, 851878.
  • Dirmeyer, P. A., X. Gao, M. Zhao, Z. Guo, T. Oki, and N. Hanasaki (2006), The Second Global Soil Wetness Project (GSWP-2): Multi-model analysis and implications for our perception of the land surface, Bull. Am. Meteorol. Soc., 87, 13811397.
  • Ek, M., K. Mitchell, L. Yin, P. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. Tarpley (2003), Implementation of Noah land-surface model advances in the NCEP operational mesoscale Eta model, J. Geophys. Res., 108(D22), 8851, doi:10.1029/2002JD003296.
  • Entin, J. K., A. Robock, K. Y. Vinnikov, S. E. Hollinger, S. Liu, and A. Namkhai (2000), Temporal and spatial scales of observed soil moisture variations in the extratropics, J. Geophys. Res., 105, 11,86511,877.
  • Guo, Z., P. A. Dirmeyer, X. Gao, and M. Zhao (2007), Improving the quality of simulated soil moisture with a multi-model ensemble approach, Q. J. R. Meteorol. Soc., 133, 731747.
  • Hansen, M. C., R. S. Defries, J. R. G. Sohlberg, and R. Sohlberg (2000), Global land cover classification at 1 km spatial resolution using a classification tree approach, Int. J. Remote Sens., 21, 13311364.
  • Henderson-Sellers, A., A. J. Pitman, P. K. Love, P. Irannejad, and T. H. Chen (1995), The project for inter-comparison of land surface parameterization schemes (PILPS): Phases 2 and 3, Bull. Am. Meteorol. Soc., 76(4), 489503.
  • Higgins, R. W., W. Shi, and E. Yarosh (2000), Improved United States precipitation quality control system and analysis, in Atlas 7, 40 pp., Natl. Cent. for Environ. Predict./Clim. Predict. Cent., Camp Springs, Md.
  • Hubbard, R. K., C. R. Berdanier, H. F. Perkins, and R. A. Leonard (1985), Characteristics of Selected Upland Soils of the Georgia Coastal Plain, 72 pp., ARS-37, USDA Agricultural Research Service, Tifton, Ga.
  • Jackson, T. J., M. H. Cosh, X. Zhan, D. D. Bosch, M. S. Seyfried, P. J. Starks, T. O. Keefer, and V. Lakshmi (2006), Validation of AMSR-E soil moisture products using watershed networks, paper presented at International Geoscience and Remote Sensing Symposium, pp. 432435, Denver, Colo., 31 July – 4 August .
  • Jarvis, P. G. (1976), The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Philos. Trans. R. Soc. Lond., B, 273, 593610.
  • Kumar, S. V., et al. (2006), LIS—An interoperable framework for high resolution land surface modeling, Environ. Model. Softw., 21, 14021415.
  • Liston, G. E., Y. C. Sud, and E. F. Wood (1994), Evaluating GCM land surface hydrology parameterizations by computing river discharge using a runoff routing model: Application to the Mississippi basin, J. Appl. Meteorol., 33, 394405.
  • Los, S. O., et al. (2000), A global 9-yr biophysical land surface data set from NOAA AVHRR data, J. Hydrometeorol., 1, 183199.
  • Luo, L., et al. (2003), Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains, J. Geophys. Res., 108(D22), 8843, doi:10.1029/2002JD003246.
  • Mahrt, L., and H.-L. Pan (1984), A two-layer model of soil hydrology, Bound.-Lay. Meteorol., 29, 120.
  • Mitchell, K. E., et al. (2004), The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.
  • Mocko, D. M., and Y. C. Sud (2001), Refinements to SSiB with an emphasis on snow physics: Evaluation and validation using GSWP and Valdai data, Earth Interact., 5, 131.
  • Mocko, D. M., G. K. Walker, and Y. C. Sud (1999), New snow-physics to complement SSiB. part II: Effects on soil moisture initialization and simulated surface fluxes, precipitation, and hydrology of GEOS II GCM, J. Meteorol. Soc. Jpn., 77(1B), 349366.
  • Oleson, K. W., et al. (2004), Technical description of the Community Land Model (CLM), NCAR Tech. Note, TN-461+STR, 174 pp., Boulder, Colo.
  • Oliveira, R., M. Oyama, and C. Nobre (2006), Incorporating hydraulic redistribution (HR) into the Simplified Simple Biosphere model (SSiB), paper presented at 8 ICXHMO, pp. 935938, Foz do Iguacu, Brazil, 24 – 28 April .
  • Perry, C. D., G. Vellidis, R. Lowrance, and D. L. Thomas (1999), Watershed-scale water quality impacts of riparian forest management, J. Water Resour. Plan. Manage., 125, 117126.
  • Pinker, R. T., et al. (2003), Surface radiation budgets in support of the GEWEX Continental Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) Project, J. Geophys. Res., 108(D22), 8844, doi:10.1029/2002JD003301.
  • Polcher, J. (2000), GLASS implementation underway, GEWEX News, 10, 9.
  • Prigent, C., F. Aires, W. B. Rossow, and A. Robock (2005), Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements, J. Geophys. Res., 110, D07110, doi:10.1029/2004JD005087.
  • Reichle, R. H., R. D. Koster, J. Dong, and A. A. Berg (2004), Global soil moisture from satellite observations, land surface models, and ground data: Implication for data assimilation, J. Hydrometeorol., 5, 430442.
  • Robock, A., et al. (2003), Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season, J. Geophys. Res., 108(D22), 8846, doi:10.1029/2002JD003245.
  • Rodell, M., et al. (2004), The global land data assimilation system, Bull. Am. Meteorol. Soc., 85(3), 381394.
  • Rodell, M., P. R. Houser, A. A. Berg, and J. S. Famiglietti (2005), Evaluation of 10 methods for initializing a land surface model, J. Hydrometeorol., 6, 146155.
  • Sahoo, A. K., P. R. Houser, C. Ferguson, E. F. Wood, P. A. Dirmeyer, and M. Kafatos (2008), Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia, Remote Sens. Environ., 112(6), 31423152.
  • Schaake, J. C., et al. (2004), An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS), J. Geophys. Res., 109, D01S90, doi:10.1029/2002JD003309.
  • Schaefer, G. L. and R. F. Paetzold (2001), SNOTEL (SNOwpack TELemetry) and SCAN (Soil Climate Analysis Network), in Proc. Intl. Workshop on Automated Wea. Stations for Appl. in Agr. and Water Resour Mgmt., World Meteorological Organization, AGM-3 WMO/TD 1074. ( ftp://ftp.wcc.nrcs.usda.gov/downloads/factpub/soils/SNOTEL-SCAN.pdf).
  • Schlosser, C. A., A. G. Slater, A. Robock, A. J. Pitman, K. Y. Vinnikov, A. Henderson-Sellers, N. A. Speranskaya, K. Mitchell, and the PIPLS 2(d) contributors (2000), Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS Phase 2 (d), Mon. Weather Rev., 128, 301321.
  • Sellers, P. J., and J. L. Dorman (1987), Testing the Simple Biosphere model (SiB) using point micrometeorological and biophysical data, J. Clim. Appl. Meteorol., 26, 622651.
  • Sellers, P. J., C. J. Tucker, G. J. Collatz, S. O. Los, C. O. Justice, D. A. Dazlich, and D. A. Randall (1996), A revised land surface parameterization (SiB2) for atmospheric GCMS. part II: The generation of global fields of terrestrial biophysical parameters from satellite data, J. Clim., 9, 706737.
  • Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Delcher (1986), A Simple Biosphere model (SiB) for use within general circulation models, J. Atmos. Sci., 43, 505531.
  • Sheridan, J. M. (1997), Rainfall-streamflow relations for coastal plain watersheds, Appl. Eng. Agric., 13(3), 333344.
  • Sheridan, J., and V. A. Ferreira (1992), Physical characteristic and geomorphic data for Little River Watersheds, Georgia, in USDA-ARS, Southeast Watershed Research Lab., Rep. 099201, 19 pp., US Department of Agriculture-Agricultural Research Service (USDA-ARS),, Tifton, GA.,
  • Sud, Y. C., and D. M. Mocko (1999), New snow-physics to complement SSiB. part I: Design and evaluation with ISLSCP Initiative I data sets, J. Meteorol. Soc. Jpn., 77, 335348.
  • Vinnikov, K. Y., and I. B. Yeserkepova (1991), Soil moisture: Empirical data and model results, J. Clim., 4, 6679.
  • Wigneron, J.-P., A. Chanzy, J.-C. Calvet, and N. Bruguier (1995), A simple algorithm to retrieve soil moisture and vegetation biomass using passive microwave measurements over crop fields, Remote Sens. Environ., 51, 331341.
  • Williams, R. G. (1982), Little River watersheds land use characteristics, in USDA-ARS, SEWRL Lab.,Note 098201, 20 pp., US Department of Agriculture-Agricultural Research Service (USDA-ARS),, Tifton, Ga.,
  • Wood, E., et al. (1998), The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2 (c) Red-Arkansas River basin experiment. I: Experimental description and summary intercomparisons, Global Planet. Change, 19, 115136.
  • Xue, Y., F. J. Zeng, and C. A. Schlosser (1996), Sensitivity to soil properties—A case study using HAPEX-Mobility data, Global Planet. Change, 13, 183194.
  • Xue, Y., P. J. Sellers, J. L. Kinter III, and J. Shukla (1991), A simplified biosphere model for global climate studies, J. Clim., 4, 345364.
  • Yang, W., et al. (2006), MODIS leaf area index products: From validation to algorithm improvement, IEEE Trans. Geosci. Remote Sens., 44(7), 18851898.
  • Zhan, X., P. R. Houser, J. P. Walker, and M. Rodell (2004), Validation of AMSR-E soil moisture product using data assimilation techniques, paper presented at COAA 2004, Beijing, 28 – 30 June .
  • Zobler, L. (1986), A world soil file for global climate modeling, NASA Tech. Memorandum 87802, 32 pp.