• Anderson, M. C., , Norman J. M. , , Diak G. R. , , Kustas W. P. , , and Mecikalski J. R. , 1997: A two-source time-integrated model for estimating surface fluxes from thermal infrared satellite observations. Remote Sens. Environ., 60 , 195216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., , Neale C. M. , , Li F. , , Norman J. M. , , Kustas W. P. , , Jayanthi H. , , and Chavez J. , 2004: Upscaling ground observations of vegetation cover and water content during SMEX02 using aircraft and Landsat imagery. Remote Sens. Environ., 90 , 447464.

    • Search Google Scholar
    • Export Citation
  • Bastiaanssen, W., , Menenti M. , , Feddes R. , , and Holtslag A. , 1998: A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J. Hydrol., 212–213 , 198212.

    • Search Google Scholar
    • Export Citation
  • Bindlish, R., , Kustas W. P. , , French A. N. , , Diak G. R. , , and Mecikalski J. R. , 2001: Influence of near-surface soil moisture on regional scale heat fluxes: Model results using microwave remote sensing data from SGP97. IEEE Trans. Geosci. Remote Sens., 39 , 17191728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brotzge, J. A., , and Crawford K. C. , 2003: Examination of the surface energy budget: A comparison of eddy correlation and Bowen ratio measurement systems. J. Hydrometeor., 4 , 160178.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, G. S., , and Norman J. M. , 1998: An Introduction to Environmental Biophysics. Springer-Verlag, 286 pp.

  • Castelli, F., , Entekhabi D. , , and Caporali E. , 1999: Estimation of surface heat flux and an index of soil moisture using adjoint-state surface energy balance. Water Resour. Res., 35 , 31153125.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choudhury, B. J., , Ahmed N. U. , , Idso S. B. , , Reginato R. J. , , and Daughtry C. , 1994: Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ., 50 , 117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosby, B. J., , Hornberger G. M. , , Clapp R. B. , , and Ginn T. R. , 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20 , 682690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crow, W. T., , Wood E. F. , , and Pan M. , 2003: Multi-objective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals. J. Geophys. Res., 108 .4725, doi:10.1029/2002JD003292.

    • Search Google Scholar
    • Export Citation
  • Crow, W. T., , Ryu D. , , and Famiglietti J. S. , 2005: Upscaling of field-scale soil moisture measurements using distributed land surface modeling. Adv. Water Resour., 28 , 15.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, Y., and Coauthors, 2003: The Common Land Model (CLM). Bull. Amer. Meteor. Soc., 84 , 10131023.

  • Diak, G. R., , Mecikalski J. R. , , Anderson M. C. , , Norman J. M. , , Kustas W. P. , , Torn R. D. , , and DeWolf R. L. , 2004: Estimating land surface energy budgets from space: Review and current efforts at the University of Wisconsin—Madison and USDA–ARS. Bull. Amer. Meteor. Soc., 85 , 6578.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Famiglietti, J. S., , and Wood E. F. , 1994: Multiscale modeling of spatially variable water and energy balance processes. Water Resour. Res., 30 , 30613078.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feddes, R. A., , and Rijetma P. E. , 1972: Water withdrawal by plant roots. J. Hydrol., 17 , 3359.

  • Franks, S. W., , and Beven K. J. , 1999: Conditioning a multi-patch SVAT model using uncertain time-space estimates of latent heat fluxes as inferred from remotely-sensed data. Water Resour. Res., 35 , 27512761.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • French, A. N., , Schmugge T. J. , , Kustas W. P. , , Brubaker K. L. , , and Prueger J. , 2003: Surface energy fluxes over El Reno, Oklahoma using high-resolution remotely sensed data. Water Resour. Res., 39 .1164, doi:10.1029/2002WR001734.

    • Search Google Scholar
    • Export Citation
  • Goudriaan, J., 1977: Crop Micrometeorology: A Simulation Study. Center for Agricultural Publication and Documents, Wageningen, 249 pp.

  • Gupta, H. V., , Bastidas L. A. , , Sorooshian S. , , Schuttleworth W. J. , , and Yang Z. L. , 1999: Parameter estimation of a land surface scheme using multicriteria methods. J. Geophys. Res., 104 , 1949119503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson-Sellers, A., , Irannejad P. , , McGuffie K. , , and Pitman A. J. , 2003: Predicting land-surface climates-better skill or moving targets? J. Geophys. Lett., 30 .1777, doi:10.1029/2003GL017387.

    • Search Google Scholar
    • Export Citation
  • Jiang, L., , and Islam S. , 2001: Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Resour. Res., 37 , 329340.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , Suarez M. J. , , Ducharne A. , , Stieglitz M. , , and Kumar P. , 2000: A catchment-based approach to modeling land surfaces processes in a general circulation model, 1: Model structure. J. Geophys. Res., 105 , 2480924822.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 1996: Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrol. Sci. J., 41 , 495516.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 1997: A two-source approach for estimating turbulent energy fluxes using multiple angle thermal infrared observations. Water Resour. Res., 33 , 14951508.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 1999a: Reply to comments about the basic equations of dual-source vegetation-atmosphere transfer models. Agric. For. Meteor., 94 , 275278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 1999b: Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperature for partial canopy cover. Agric. For. Meteor., 94 , 1329.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 2000a: A two-source energy balance approach using directional radiometric temperature observations for sparse canopy covered surfaces. Agron. J., 92 , 847854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 2000b: Evaluating the effects of subpixel heterogeneity on pixel average fluxes. Remote Sens. Environ., 74 , 327342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , Zhan X. , , and Schmugge T. J. , 1998: Combining optical and microwave remote sensing for mapping energy fluxes in a semiarid watershed. Remote Sens. Environ., 64 , 116131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , Hatfield J. L. , , and Prueger J. H. , 2005: The Soil Moisture–Atmosphere Coupling Experiment (SMACEX): Background, hydrometeorological conditions, and preliminary findings. J. Hydrometeor., 6 , 791804.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, F., , Kustas W. P. , , Prueger J. H. , , Neale C. M. U. , , and Jackson T. J. , 2005: Utility of remote sensing–based two-source energy balance model under low- and high-vegetation cover conditions. J. Hydrometeor., 6 , 878891.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., , Lettenmaier D. P. , , Wood E. F. , , and Burges S. J. , 1994: A simple hydrologically based model of land surface water and energy fluxes for GCMs. J. Geophys. Res., 99 , 1441514428.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., , Diak G. R. , , Anderson M. C. , , and Norman J. M. , 1999: Estimating fluxes on continental scale using remotely sensed data in at atmospheric–land exchange model. J. Appl. Meteor., 38 , 221247.

    • Search Google Scholar
    • Export Citation
  • Mitchell, K. E., and Coauthors, 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.

    • Search Google Scholar
    • Export Citation
  • Norman, J. M., , Kustas W. P. , , and Humes K. S. , 1995: A two-source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. For. Meteor., 77 , 263293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., , Zion M. S. , , and Wood E. F. , 1997: A soil-vegetation-atmosphere transfer scheme for modeling spatially variable water and energy balance processes. J. Geophys. Res., 102 , 43034324.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., and Coauthors, 1999: Key results and implications form phase 1(c) of the Project for Intercomparison of Land-surface Parameterization Schemes. Climate Dyn., 15 , 673684.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Priestley, C. H. B., , and Taylor R. J. , 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100 , 8192.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prueger, J. H., and Coauthors, 2005: Tower and aircraft eddy covariance measurements of water, energy, and carbon dioxide fluxes during SMACEX. J. Hydrometeor., 6 , 954960.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85 , 381394.

  • Sauer, T. J., , Norman J. M. , , Tanner C. B. , , and Wilson T. B. , 1995: Measurement of heat and vapor transfer at the soil surface beneath a maize canopy using source plates. Agric. For. Meteor., 75 , 161189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schuurmans, J. M., , Troch P. A. , , Veldhuizen A. A. , , Bastiaanssen W. G. M. , , and Bierkens M. F. P. , 2003: Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv. Water. Resour., 26 , 151159.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, Z., 2002: The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci., 6 , 8599.

  • Wetzel, P. J., , and Chang J. , 1987: Concerning the relationship between evapotranspiration and soil moisture. J. Climate Appl. Meteor., 26 , 1827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wetzel, P. J., , and Chang J. , 1988: Evapotranspiration from nonuniform surfaces: A first approach for short-term numerical weather prediction. Mon. Wea. Rev., 116 , 600621.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, E. F., and Coauthors, 1998: The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(c) Red-Arkansas river basin experiment: 1. Experiment description and summary intercomparisons. Global Planet. Change, 19 , 115135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhan, X., , Kustas W. P. , , and Humes K. S. , 1996: An intercomparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature. Remote Sens. Environ., 58 , 242256.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Intercomparison of Spatially Distributed Models for Predicting Surface Energy Flux Patterns during SMACEX

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  • 1 Hydrology and Remote Sensing Laboratory, ARS, USDA, Beltsville, Maryland
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Abstract

The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.

Corresponding author address: W. T. Crow, Hydrology and Remote Sensing Laboratory, USDA ARS, Rm. 104, Bldg. 007, BARC-W, Beltsville, MD 20705. Email: wcrow@hydrolab.arsudsa.gov

Abstract

The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.

Corresponding author address: W. T. Crow, Hydrology and Remote Sensing Laboratory, USDA ARS, Rm. 104, Bldg. 007, BARC-W, Beltsville, MD 20705. Email: wcrow@hydrolab.arsudsa.gov

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